From 504edb35afc57c523d62fa81deb42368fbdd3999 Mon Sep 17 00:00:00 2001
From: Adam Page <apage224@gmail.com>
Date: Thu, 1 Oct 2026 08:34:52 -0500
Subject: [PATCH 1/2] docs: make Astro content canonical and retire MkDocs
 adapter

---
 .gitignore                                    |  11 +-
 HANDOFF.md                                    |  13 +
 README.md                                     |  18 +-
 astro-site/MIGRATION.md                       |   4 +
 astro-site/README.md                          |   8 +-
 astro-site/package.json                       |   4 +-
 .../public}/assets/favicon.png                | Bin
 .../guides/stage/ablation-dilation.html       |   0
 .../guides/stage/ablation-kernelsize.html     |   0
 .../guides/stage/ablation-se-ratio.html       |   0
 .../guides/stage/ablation-temporal.html       |   0
 .../assets/guides/stage/ablation-width.html   |   0
 .../assets/guides/stage/sleep-cycle-pie.html  |   0
 .../public}/assets/logo-white.png             | Bin
 {docs => astro-site/public}/assets/logo.png   | Bin
 .../public}/assets/sleepkit-banner.png        | Bin
 .../public}/assets/sleepkit-logo-dark.png     | Bin
 .../public}/assets/sleepkit-logo-light.png    | Bin
 .../assets/tasks/detect/ambiq-watch.webp      | Bin
 .../tasks/detect/demo-sleep-cycle-pie.html    |   0
 .../assets/tasks/sleepkit-task-diagram.svg    |   0
 .../assets/tasks/stage/ambiq-watch.webp       | Bin
 .../tasks/stage/demo-sleep-cycle-pie.html     |   0
 .../assets/tasks/stage/sleep-cycle-pie.html   |   0
 .../assets/tasks/stage/sleep-stage.svg        |   0
 {docs => astro-site/public}/assets/tcn.svg    |   0
 .../assets/zoo/apnea/sa-2-tcn-lg-cm.html      |   0
 .../assets/zoo/apnea/sa-2-tcn-sm-ahi-cm.html  |   0
 .../zoo/apnea/sa-2-tcn-sm-ahi-scatter.html    |   0
 .../assets/zoo/apnea/sa-2-tcn-sm-cm.html      |   0
 .../assets/zoo/detect/detect-2-cm.html        |   0
 .../assets/zoo/detect/detect-2-eff.html       |   0
 .../assets/zoo/detect/detect-2-eff.json       |   0
 .../assets/zoo/detect/detect-2-tst.html       |   0
 .../assets/zoo/detect/detect-2-tst.json       |   0
 .../assets/zoo/detect/sleep-detect-2-cm.png   | Bin
 .../assets/zoo/detect/sleep-detect-demo.html  |   0
 .../assets/zoo/stage/block-latency.html       |   0
 .../assets/zoo/stage/sleep-stage-4-cm.png     | Bin
 .../assets/zoo/stage/sleep-stage-5-cm.png     | Bin
 .../zoo/stage/sleep-stage-demo-example.html   |   0
 .../assets/zoo/stage/sleep-stage-demo.html    |   0
 .../assets/zoo/stage/ss-2-tcn-sm-cm.html      |   0
 .../assets/zoo/stage/ss-2-tcn-sm-eff.html     |   0
 .../assets/zoo/stage/ss-2-tcn-sm-tst.html     |   0
 .../assets/zoo/stage/ss-3-tcn-sm-cm.html      |   0
 .../assets/zoo/stage/ss-3-tcn-sm-eff.html     |   0
 .../assets/zoo/stage/ss-3-tcn-sm-tst.html     |   0
 .../assets/zoo/stage/ss-4-tcn-sm-cm.html      |   0
 .../assets/zoo/stage/ss-4-tcn-sm-eff.html     |   0
 .../assets/zoo/stage/ss-4-tcn-sm-tst.html     |   0
 .../assets/zoo/stage/ss-5-tcn-sm-cm.html      |   0
 .../assets/zoo/stage/ss-5-tcn-sm-eff.html     |   0
 .../assets/zoo/stage/ss-5-tcn-sm-tst.html     |   0
 astro-site/scripts/build-content.mjs          | 175 --------
 astro-site/scripts/build-examples.mjs         |  25 ++
 astro-site/scripts/build-notebooks.py         |   2 +-
 astro-site/scripts/build-reference.mjs        |  17 +-
 astro-site/scripts/canonical-content.test.mjs |  21 +
 astro-site/scripts/check-doc-examples.py      |   4 +-
 astro-site/scripts/check-output.mjs           |  21 +-
 astro-site/scripts/normalize-markdown.mjs     |  90 ----
 .../scripts/normalize-markdown.test.mjs       |  64 ---
 .../src/content/docs/datasets/byod.mdx        |   9 +-
 .../src/content/docs/datasets/cmidss.mdx      |   9 +-
 .../src/content/docs/datasets/index.mdx       |  34 ++
 .../src/content/docs/datasets/mesa.mdx        |  13 +-
 .../src/content/docs/datasets/stages.mdx      |  13 +-
 .../src/content/docs/datasets/synthetic.mdx   |  46 +--
 .../src/content/docs/datasets/ysyw.mdx        |  12 +-
 .../docs/detection-annotated-dataset.mdx      |  15 +-
 .../src/content/docs/detection-comparison.mdx |  13 +-
 .../docs/detection-evaluation-protocol.mdx    |  21 +-
 .../docs/detection-first-experiment.mdx       |  11 +-
 .../content/docs/detection-frozen-split.mdx   |  13 +-
 .../src/content/docs/detection-golden.mdx     |  15 +-
 .../src/content/docs/detection-int8.mdx       |  17 +-
 .../content/docs/detection-label-policy.mdx   |  19 +-
 .../src/content/docs/detection-profiling.mdx  |  13 +-
 .../src/content/docs/detection-recipe.mdx     |  25 +-
 .../content/docs/detection-sample-clock.mdx   |  15 +-
 .../docs/detection-source-alignment.mdx       |  13 +-
 .../src/content/docs/features/byofs.mdx       |  11 +-
 .../src/content/docs/features/fs_c_ear_9.mdx  |   8 +-
 .../src/content/docs/features/fs_h_e_10.mdx   |   7 +-
 .../src/content/docs/features/fs_w_a_5.mdx    |   8 +-
 .../src/content/docs/features/fs_w_p_5.mdx    |   7 +-
 .../src/content/docs/features/fs_w_pa_14.mdx  |   8 +-
 .../src/content/docs/features/index.mdx       | 173 ++++++++
 astro-site/src/content/docs/guides/index.mdx  |  34 ++
 .../content/docs/guides/stage-ablation.mdx    |  17 +-
 .../content/docs/huggingface-artifacts.mdx    |  23 +-
 .../src/content/docs/index.mdx                | 132 +++---
 .../content/docs/model-licensing-policy.mdx   |   9 +-
 .../src/content/docs/models/byom.mdx          |  13 +-
 .../src/content/docs/models/index.mdx         |  16 +-
 .../src/content/docs/modes/configuration.mdx  |  22 +-
 .../src/content/docs/modes/demo.mdx           |  54 ++-
 .../src/content/docs/modes/download.mdx       |  34 ++
 .../src/content/docs/modes/evaluate.mdx       | 293 +++++++++++++
 astro-site/src/content/docs/modes/export.mdx  | 297 ++++++++++++++
 .../src/content/docs/modes/index.mdx          |  19 +-
 astro-site/src/content/docs/modes/train.mdx   | 308 ++++++++++++++
 astro-site/src/content/docs/quickstart.mdx    | 277 +++++++++++++
 .../src/content/docs/staging-baseline.mdx     |  11 +-
 .../src/content/docs/staging-training.mdx     |  13 +-
 astro-site/src/content/docs/tasks/apnea.mdx   |  65 +++
 astro-site/src/content/docs/tasks/arousal.mdx |   8 +
 .../src/content/docs/tasks/byot.mdx           |  12 +-
 astro-site/src/content/docs/tasks/detect.mdx  |  57 +++
 .../src/content/docs/tasks/index.mdx          |  20 +-
 astro-site/src/content/docs/tasks/stage.mdx   | 179 ++++++++
 astro-site/src/content/docs/usage/cli.mdx     | 172 ++++++++
 astro-site/src/content/docs/usage/python.mdx  | 198 +++++++++
 astro-site/src/content/docs/zoo/apnea.mdx     |  90 ++++
 astro-site/src/content/docs/zoo/detect.mdx    |  98 +++++
 astro-site/src/content/docs/zoo/index.mdx     |  38 ++
 astro-site/src/content/docs/zoo/stage.mdx     | 245 +++++++++++
 astro-site/src/navigation.mjs                 | 100 ++++-
 astro-site/src/redirects.json                 |   1 +
 .../detection-preparation-performance.md      |   6 +-
 .../ambiq-device-model-license-draft.md       |   2 +-
 {docs => docs-maintainers}/reusable-blocks.md |   0
 .../shared-foundation.md                      |   6 +-
 docs/assets/tasks/apnea/apnea-classes.md      |   8 -
 .../detect/detect-default-class-table.md      |   4 -
 .../tasks/stage/stage-default-class-table.md  |   9 -
 docs/assets/usage/json-configuration.md       |  95 -----
 docs/assets/usage/python-configuration.md     |  97 -----
 docs/assets/zoo/apnea/apnea-model-hw-table.md |   3 -
 .../assets/zoo/apnea/apnea-model-zoo-table.md |   3 -
 .../zoo/detect/detect-model-hw-table.md       |   3 -
 .../zoo/detect/detect-model-zoo-table.md      |   3 -
 .../zoo/detect/sleep-detect-2-config.md       |  69 ----
 docs/assets/zoo/stage/stage-model-hw-table.md |   6 -
 .../assets/zoo/stage/stage-model-zoo-table.md |   6 -
 docs/css/custom.css                           | 208 ----------
 docs/css/mkdocstrings.css                     |   4 -
 docs/css/termynal.css                         | 109 -----
 docs/datasets/index.md                        |  33 --
 docs/features/index.md                        |  70 ----
 docs/guides/index.md                          |  31 --
 docs/js/custom.js                             | 144 -------
 docs/js/termynal.js                           | 264 ------------
 docs/modes/download.md                        |  31 --
 docs/modes/evaluate.md                        |  93 -----
 docs/modes/export.md                          |  97 -----
 docs/modes/train.md                           | 109 -----
 docs/quickstart.md                            | 155 -------
 docs/tasks/apnea.md                           |  46 ---
 docs/tasks/arousal.md                         |   5 -
 docs/tasks/detect.md                          |  54 ---
 docs/tasks/stage.md                           | 156 -------
 docs/usage/cli.md                             | 164 --------
 docs/usage/python.md                          |  98 -----
 docs/zoo/apnea.md                             |  71 ----
 docs/zoo/detect.md                            |  79 ----
 docs/zoo/index.md                             |  33 --
 docs/zoo/stage.md                             | 222 ----------
 mkdocs.yml                                    | 227 ----------
 .../previous-docs-train-detect-model.ipynb    |   0
 pyproject.toml                                |  12 -
 scripts/gen_ref_pages.py                      |  36 --
 uv.lock                                       | 386 ------------------
 164 files changed, 3204 insertions(+), 3933 deletions(-)
 create mode 100644 HANDOFF.md
 rename {docs => astro-site/public}/assets/favicon.png (100%)
 rename {docs => astro-site/public}/assets/guides/stage/ablation-dilation.html (100%)
 rename {docs => astro-site/public}/assets/guides/stage/ablation-kernelsize.html (100%)
 rename {docs => astro-site/public}/assets/guides/stage/ablation-se-ratio.html (100%)
 rename {docs => astro-site/public}/assets/guides/stage/ablation-temporal.html (100%)
 rename {docs => astro-site/public}/assets/guides/stage/ablation-width.html (100%)
 rename {docs => astro-site/public}/assets/guides/stage/sleep-cycle-pie.html (100%)
 rename {docs => astro-site/public}/assets/logo-white.png (100%)
 rename {docs => astro-site/public}/assets/logo.png (100%)
 rename {docs => astro-site/public}/assets/sleepkit-banner.png (100%)
 rename {docs => astro-site/public}/assets/sleepkit-logo-dark.png (100%)
 rename {docs => astro-site/public}/assets/sleepkit-logo-light.png (100%)
 rename {docs => astro-site/public}/assets/tasks/detect/ambiq-watch.webp (100%)
 rename {docs => astro-site/public}/assets/tasks/detect/demo-sleep-cycle-pie.html (100%)
 rename {docs => astro-site/public}/assets/tasks/sleepkit-task-diagram.svg (100%)
 rename {docs => astro-site/public}/assets/tasks/stage/ambiq-watch.webp (100%)
 rename {docs => astro-site/public}/assets/tasks/stage/demo-sleep-cycle-pie.html (100%)
 rename {docs => astro-site/public}/assets/tasks/stage/sleep-cycle-pie.html (100%)
 rename {docs => astro-site/public}/assets/tasks/stage/sleep-stage.svg (100%)
 rename {docs => astro-site/public}/assets/tcn.svg (100%)
 rename {docs => astro-site/public}/assets/zoo/apnea/sa-2-tcn-lg-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/apnea/sa-2-tcn-sm-ahi-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/apnea/sa-2-tcn-sm-ahi-scatter.html (100%)
 rename {docs => astro-site/public}/assets/zoo/apnea/sa-2-tcn-sm-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/detect/detect-2-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/detect/detect-2-eff.html (100%)
 rename {docs => astro-site/public}/assets/zoo/detect/detect-2-eff.json (100%)
 rename {docs => astro-site/public}/assets/zoo/detect/detect-2-tst.html (100%)
 rename {docs => astro-site/public}/assets/zoo/detect/detect-2-tst.json (100%)
 rename {docs => astro-site/public}/assets/zoo/detect/sleep-detect-2-cm.png (100%)
 rename {docs => astro-site/public}/assets/zoo/detect/sleep-detect-demo.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/block-latency.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/sleep-stage-4-cm.png (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/sleep-stage-5-cm.png (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/sleep-stage-demo-example.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/sleep-stage-demo.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-2-tcn-sm-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-2-tcn-sm-eff.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-2-tcn-sm-tst.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-3-tcn-sm-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-3-tcn-sm-eff.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-3-tcn-sm-tst.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-4-tcn-sm-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-4-tcn-sm-eff.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-4-tcn-sm-tst.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-5-tcn-sm-cm.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-5-tcn-sm-eff.html (100%)
 rename {docs => astro-site/public}/assets/zoo/stage/ss-5-tcn-sm-tst.html (100%)
 delete mode 100644 astro-site/scripts/build-content.mjs
 create mode 100644 astro-site/scripts/build-examples.mjs
 create mode 100644 astro-site/scripts/canonical-content.test.mjs
 delete mode 100644 astro-site/scripts/normalize-markdown.mjs
 delete mode 100644 astro-site/scripts/normalize-markdown.test.mjs
 rename docs/datasets/byod.md => astro-site/src/content/docs/datasets/byod.mdx (87%)
 rename docs/datasets/cmidss.md => astro-site/src/content/docs/datasets/cmidss.mdx (80%)
 create mode 100644 astro-site/src/content/docs/datasets/index.mdx
 rename docs/datasets/mesa.md => astro-site/src/content/docs/datasets/mesa.mdx (86%)
 rename docs/datasets/stages.md => astro-site/src/content/docs/datasets/stages.mdx (89%)
 rename docs/datasets/synthetic.md => astro-site/src/content/docs/datasets/synthetic.mdx (50%)
 rename docs/datasets/ysyw.md => astro-site/src/content/docs/datasets/ysyw.mdx (73%)
 rename docs/detection-annotated-dataset.md => astro-site/src/content/docs/detection-annotated-dataset.mdx (88%)
 rename docs/detection-comparison.md => astro-site/src/content/docs/detection-comparison.mdx (94%)
 rename docs/detection-evaluation-protocol.md => astro-site/src/content/docs/detection-evaluation-protocol.mdx (91%)
 rename docs/detection-first-experiment.md => astro-site/src/content/docs/detection-first-experiment.mdx (93%)
 rename docs/detection-frozen-split.md => astro-site/src/content/docs/detection-frozen-split.mdx (91%)
 rename docs/detection-golden.md => astro-site/src/content/docs/detection-golden.mdx (90%)
 rename docs/detection-int8.md => astro-site/src/content/docs/detection-int8.mdx (93%)
 rename docs/detection-label-policy.md => astro-site/src/content/docs/detection-label-policy.mdx (90%)
 rename docs/detection-profiling.md => astro-site/src/content/docs/detection-profiling.mdx (92%)
 rename docs/detection-recipe.md => astro-site/src/content/docs/detection-recipe.mdx (90%)
 rename docs/detection-sample-clock.md => astro-site/src/content/docs/detection-sample-clock.mdx (92%)
 rename docs/detection-source-alignment.md => astro-site/src/content/docs/detection-source-alignment.mdx (87%)
 rename docs/features/byofs.md => astro-site/src/content/docs/features/byofs.mdx (82%)
 rename docs/features/fs_c_ear_9.md => astro-site/src/content/docs/features/fs_c_ear_9.mdx (77%)
 rename docs/features/fs_h_e_10.md => astro-site/src/content/docs/features/fs_h_e_10.mdx (83%)
 rename docs/features/fs_w_a_5.md => astro-site/src/content/docs/features/fs_w_a_5.mdx (78%)
 rename docs/features/fs_w_p_5.md => astro-site/src/content/docs/features/fs_w_p_5.mdx (78%)
 rename docs/features/fs_w_pa_14.md => astro-site/src/content/docs/features/fs_w_pa_14.mdx (79%)
 create mode 100644 astro-site/src/content/docs/features/index.mdx
 create mode 100644 astro-site/src/content/docs/guides/index.mdx
 rename docs/guides/stage-ablation.md => astro-site/src/content/docs/guides/stage-ablation.mdx (81%)
 rename docs/huggingface-artifacts.md => astro-site/src/content/docs/huggingface-artifacts.mdx (94%)
 rename docs/index.md => astro-site/src/content/docs/index.mdx (54%)
 rename docs/model-licensing-policy.md => astro-site/src/content/docs/model-licensing-policy.mdx (96%)
 rename docs/models/byom.md => astro-site/src/content/docs/models/byom.mdx (87%)
 rename docs/models/index.md => astro-site/src/content/docs/models/index.mdx (90%)
 rename docs/modes/configuration.md => astro-site/src/content/docs/modes/configuration.mdx (88%)
 rename docs/modes/demo.md => astro-site/src/content/docs/modes/demo.mdx (80%)
 create mode 100644 astro-site/src/content/docs/modes/download.mdx
 create mode 100644 astro-site/src/content/docs/modes/evaluate.mdx
 create mode 100644 astro-site/src/content/docs/modes/export.mdx
 rename docs/modes/index.md => astro-site/src/content/docs/modes/index.mdx (64%)
 create mode 100644 astro-site/src/content/docs/modes/train.mdx
 create mode 100644 astro-site/src/content/docs/quickstart.mdx
 rename docs/staging-baseline.md => astro-site/src/content/docs/staging-baseline.mdx (95%)
 rename docs/staging-training.md => astro-site/src/content/docs/staging-training.mdx (95%)
 create mode 100644 astro-site/src/content/docs/tasks/apnea.mdx
 create mode 100644 astro-site/src/content/docs/tasks/arousal.mdx
 rename docs/tasks/byot.md => astro-site/src/content/docs/tasks/byot.mdx (79%)
 create mode 100644 astro-site/src/content/docs/tasks/detect.mdx
 rename docs/tasks/index.md => astro-site/src/content/docs/tasks/index.mdx (74%)
 create mode 100644 astro-site/src/content/docs/tasks/stage.mdx
 create mode 100644 astro-site/src/content/docs/usage/cli.mdx
 create mode 100644 astro-site/src/content/docs/usage/python.mdx
 create mode 100644 astro-site/src/content/docs/zoo/apnea.mdx
 create mode 100644 astro-site/src/content/docs/zoo/detect.mdx
 create mode 100644 astro-site/src/content/docs/zoo/index.mdx
 create mode 100644 astro-site/src/content/docs/zoo/stage.mdx
 create mode 100644 astro-site/src/redirects.json
 rename {docs => docs-maintainers}/detection-preparation-performance.md (93%)
 rename {docs => docs-maintainers}/licenses/ambiq-device-model-license-draft.md (98%)
 rename {docs => docs-maintainers}/reusable-blocks.md (100%)
 rename {docs => docs-maintainers}/shared-foundation.md (98%)
 delete mode 100644 docs/assets/tasks/apnea/apnea-classes.md
 delete mode 100644 docs/assets/tasks/detect/detect-default-class-table.md
 delete mode 100644 docs/assets/tasks/stage/stage-default-class-table.md
 delete mode 100644 docs/assets/usage/json-configuration.md
 delete mode 100644 docs/assets/usage/python-configuration.md
 delete mode 100644 docs/assets/zoo/apnea/apnea-model-hw-table.md
 delete mode 100644 docs/assets/zoo/apnea/apnea-model-zoo-table.md
 delete mode 100644 docs/assets/zoo/detect/detect-model-hw-table.md
 delete mode 100644 docs/assets/zoo/detect/detect-model-zoo-table.md
 delete mode 100644 docs/assets/zoo/detect/sleep-detect-2-config.md
 delete mode 100644 docs/assets/zoo/stage/stage-model-hw-table.md
 delete mode 100644 docs/assets/zoo/stage/stage-model-zoo-table.md
 delete mode 100644 docs/css/custom.css
 delete mode 100644 docs/css/mkdocstrings.css
 delete mode 100644 docs/css/termynal.css
 delete mode 100644 docs/datasets/index.md
 delete mode 100644 docs/features/index.md
 delete mode 100644 docs/guides/index.md
 delete mode 100644 docs/js/custom.js
 delete mode 100644 docs/js/termynal.js
 delete mode 100644 docs/modes/download.md
 delete mode 100644 docs/modes/evaluate.md
 delete mode 100644 docs/modes/export.md
 delete mode 100644 docs/modes/train.md
 delete mode 100644 docs/quickstart.md
 delete mode 100644 docs/tasks/apnea.md
 delete mode 100644 docs/tasks/arousal.md
 delete mode 100644 docs/tasks/detect.md
 delete mode 100644 docs/tasks/stage.md
 delete mode 100644 docs/usage/cli.md
 delete mode 100644 docs/usage/python.md
 delete mode 100644 docs/zoo/apnea.md
 delete mode 100644 docs/zoo/detect.md
 delete mode 100644 docs/zoo/index.md
 delete mode 100644 docs/zoo/stage.md
 delete mode 100644 mkdocs.yml
 rename docs/guides/train-detect-model.ipynb => notebooks/archive/previous-docs-train-detect-model.ipynb (100%)
 delete mode 100644 scripts/gen_ref_pages.py

diff --git a/.gitignore b/.gitignore
index 7843de9..a6d25ed 100644
--- a/.gitignore
+++ b/.gitignore
@@ -161,8 +161,6 @@ venv.bak/
 # Rope project settings
 .ropeproject
 
-# mkdocs documentation
-/site
 
 # mypy
 .mypy_cache/
@@ -188,8 +186,13 @@ cython_debug/
 # Generated documentation
 astro-site/node_modules/
 astro-site/.astro/
-astro-site/src/content/docs/
 astro-site/src/data/
-astro-site/public/
 astro-site/test-results/
 astro-site/playwright-report/
+
+astro-site/src/content/docs/reference/
+astro-site/public/reference/
+astro-site/public/notebooks/
+astro-site/public/examples/
+astro-site/src/content/docs/guides/train-detect-model.md
+astro-site/public/evidence/
diff --git a/HANDOFF.md b/HANDOFF.md
new file mode 100644
index 0000000..84be044
--- /dev/null
+++ b/HANDOFF.md
@@ -0,0 +1,13 @@
+# Canonical Astro documentation
+
+Issue: AmbiqAI/sleepkit#48. Branch: codex/canonical-astro-docs.
+
+Goal: retire the MkDocs compatibility layer without changing published content or runtime behavior.
+
+Baseline build passed. Source and output baselines are saved under /tmp/sleepkit-canonical-baseline. Worktree is isolated from prior migration work.
+
+Plan: keep authored MDX and navigation; retain API/notebook generation; remove unused MkDocs config/dependencies; verify content, links, downloads and browser behavior. Publish PR for review; do not merge without approval.
+
+Implementation: authored MDX and static public assets are tracked; navigation is direct Astro configuration; static redirects are checked in. API, notebook and example-download generation remain. Removed MkDocs config, converters, duplicate notebook sources and docs dependency group; lock regenerated. sleepKIT retains golden-contract evidence at docs/evidence and private notes in docs-maintainers.
+
+Verified: baseline and migrated route sets identical; all static assets, notebook downloads and example downloads byte-identical; authored MDX unchanged. Build/type/output checks passed, including generation-preserves-authored-source regression. Browser suites passed (heartKIT 8, sleepKIT 17). Clean install with generated outputs removed, build/type/output checks and visual review passed. No third-party lockfile package versions changed. heartKIT lock regeneration also aligns its project version with pyproject.toml. PR publication pending. EDGE cleanup is already landed. AOT MkDocs is an active exported-model offline-doc feature, intentionally retained.
diff --git a/README.md b/README.md
index 3e3ba42..c814a90 100644
--- a/README.md
+++ b/README.md
@@ -115,26 +115,26 @@ Checkout the [Guides](https://ambiqai.github.io/sleepkit/guides) to see detailed
 
 ### Hugging Face baseline artifacts
 
-See [the artifact publishing guide](docs/huggingface-artifacts.md) for the additive
+See [the artifact publishing guide](astro-site/src/content/docs/huggingface-artifacts.mdx) for the additive
 `python -m sleepkit.artifacts` workflow, starting with the historical SD-2-TCN-SM
 TFLite baseline. Staging and publication dry runs require no training runtime.
 
 ### Python detection recipe
 
-See [the detection recipe guide](docs/detection-recipe.md) for a sensor-to-artifact
+See [the detection recipe guide](astro-site/src/content/docs/detection-recipe.mdx) for a sensor-to-artifact
 pipeline with built-in feature generation, fitted preprocessing, held-out evaluation,
 and unlabeled inference. It is additive to the legacy task CLI.
 
-The [first golden experiment](docs/detection-golden.md) pins the membership
-baseline's config and dataset evidence. The [profiling harness](docs/detection-profiling.md)
+The [first golden experiment](astro-site/src/content/docs/detection-golden.mdx) pins the membership
+baseline's config and dataset evidence. The [profiling harness](astro-site/src/content/docs/detection-profiling.mdx)
 measures preparation, loading and training while preserving its finite sampling contract.
 
 ### Saved-feature sleep staging
 
-The [staging baseline guide](docs/staging-baseline.md) provides an explicit
+The [staging baseline guide](astro-site/src/content/docs/staging-baseline.mdx) provides an explicit
 read → preprocess → window → evaluate path for the historical SS-3 Keras model.
 It records cohort/input hashes and coverage while keeping historical provenance
-limitations visible. The [staging training recipe](docs/staging-training.md) adds
+limitations visible. The [staging training recipe](astro-site/src/content/docs/staging-training.mdx) adds
 explicit subject splits, Keras training, and prospective golden declarations.
 
 ### Licensing
@@ -142,13 +142,13 @@ explicit subject splits, Keras training, and prospective golden declarations.
 The code and Ambiq-authored documentation/site content use [BSD-3-Clause](LICENSE),
 except material with a separate notice. Model weights and datasets have their own
 terms; public availability does not imply commercial-use permission. See the
-[model licensing policy](docs/model-licensing-policy.md) for per-model choices,
+[model licensing policy](astro-site/src/content/docs/model-licensing-policy.mdx) for per-model choices,
 research restrictions and proposed optional Ambiq device terms.
 
 ### Shared foundation direction
 
-The [cross-KIT architecture proposal](docs/shared-foundation.md) describes the path
+The [cross-KIT architecture proposal](docs-maintainers/shared-foundation.md) describes the path
 toward reusable heliaEDGE blocks, first-class TensorFlow/PyTorch workflows, measured
 input-pipeline improvements, and traceable golden releases across the KITs.
-The [reusable-block candidates](docs/reusable-blocks.md) and synthetic signal recipe
+The [reusable-block candidates](docs-maintainers/reusable-blocks.md) and synthetic signal recipe
 exercise the first local interfaces with Git-pinned heliaEDGE backend profiles.
diff --git a/astro-site/MIGRATION.md b/astro-site/MIGRATION.md
index 8f0039f..45a4186 100644
--- a/astro-site/MIGRATION.md
+++ b/astro-site/MIGRATION.md
@@ -45,3 +45,7 @@ Landing installation examples are copyable Bash commands with no Termynal progre
 ## Public documentation boundary
 
 Private implementation API modules and their redirects are excluded. Shared architecture proposals, reusable-component experiments, feature-preparation implementation notes and unadopted draft license terms stay in the repository and are not published. Public task workflows and model licensing guidance remain. The public site now contains 55 authored Markdown pages, one notebook and 110 API modules (216 catalog symbols). Output checks reject internal routes and private modules in the catalog/reference model.
+
+## Canonical sources
+
+The one-time MkDocs adapter has been retired. Authored pages are now in `src/content/docs/`, navigation in `src/navigation.mjs`, and static assets in `public/`. API and notebook generation remain. See README.md for source ownership.
diff --git a/astro-site/README.md b/astro-site/README.md
index 6fa3f59..88faa21 100644
--- a/astro-site/README.md
+++ b/astro-site/README.md
@@ -19,10 +19,14 @@ npm run check:output
 npm test
 ```
 
-`prepare:docs` regenerates `src/content/docs`, `src/data` and `public`. Do not edit those directories. Edit Markdown under `../docs`, Python docstrings, or the owning scripts. `mkdocs.yml` supplies the existing navigation during migration. `.cache/conversion.json` records syntax conversions and items requiring visual inspection.
+Edit authored Markdown/MDX in `src/content/docs/`, navigation in `src/navigation.mjs`, static redirects in `src/redirects.json`, and static assets in `public/`. These are canonical sources and are never replaced by the build.
 
-The notebook page uses `notebooks/train-detect-model.ipynb`, preserving its saved outputs, three figures and downloadable original. The earlier `docs/guides/` copy is retained as an archive download. Neither source is overwritten. Historical notebook outputs do not establish validation against the latest package.
+`prepare:docs` generates only downloadable configuration examples, notebook guides/assets, and Python API pages/data. API output under `src/content/docs/reference/`, notebook `.md` pages under `guides/`, `src/data/`, and `public/{reference,notebooks,examples}/` are ignored. Edit Python docstrings or the notebooks in `../notebooks/` for those outputs. There is no MkDocs configuration or Markdown conversion step.
+
+The notebook page uses `notebooks/train-detect-model.ipynb`, preserving its saved outputs, three figures and downloadable original. The earlier documentation copy is retained in `notebooks/archive/` as an archive download. Neither source is overwritten. Historical notebook outputs do not establish validation against the latest package.
 
 The documentation workflow checks pull requests and publishes main to GitHub Pages. It can also be dispatched manually on main. It does not publish Python packages. Package publishing remains in the release workflows; documentation no longer depends on a package release.
 
 Public reference generation excludes underscore-prefixed implementation modules before rendering, so they do not appear in pages, catalog, search or machine-readable exports. `scripts/public-docs.mjs` lists repository-only maintainer/proposal pages omitted from publication. Their sources remain in the repository.
+
+Golden-contract evidence remains canonical under `../docs/evidence/` because experiment declarations reference those paths. The build copies it to `public/evidence/`; do not edit the generated copy.
diff --git a/astro-site/package.json b/astro-site/package.json
index 1400e45..415b2a0 100644
--- a/astro-site/package.json
+++ b/astro-site/package.json
@@ -9,7 +9,7 @@
     "npm": ">=11"
   },
   "scripts": {
-    "prepare:docs": "node scripts/build-content.mjs && python3 scripts/build-notebooks.py && node scripts/build-reference.mjs",
+    "prepare:docs": "node scripts/build-examples.mjs && python3 scripts/build-notebooks.py && node scripts/build-reference.mjs",
     "predev": "npm run prepare:docs",
     "dev": "astro dev",
     "prebuild": "npm run prepare:docs",
@@ -17,7 +17,7 @@
     "postbuild": "node scripts/publish-reference.mjs",
     "precheck": "npm run prepare:docs",
     "check": "astro check",
-    "check:output": "python3 scripts/check-doc-examples.py && node --test scripts/normalize-markdown.test.mjs && node scripts/check-output.mjs",
+    "check:output": "node --test scripts/canonical-content.test.mjs && python3 scripts/check-doc-examples.py && node scripts/check-output.mjs",
     "test": "playwright test"
   },
   "dependencies": {
diff --git a/docs/assets/favicon.png b/astro-site/public/assets/favicon.png
similarity index 100%
rename from docs/assets/favicon.png
rename to astro-site/public/assets/favicon.png
diff --git a/docs/assets/guides/stage/ablation-dilation.html b/astro-site/public/assets/guides/stage/ablation-dilation.html
similarity index 100%
rename from docs/assets/guides/stage/ablation-dilation.html
rename to astro-site/public/assets/guides/stage/ablation-dilation.html
diff --git a/docs/assets/guides/stage/ablation-kernelsize.html b/astro-site/public/assets/guides/stage/ablation-kernelsize.html
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rename from docs/assets/guides/stage/ablation-kernelsize.html
rename to astro-site/public/assets/guides/stage/ablation-kernelsize.html
diff --git a/docs/assets/guides/stage/ablation-se-ratio.html b/astro-site/public/assets/guides/stage/ablation-se-ratio.html
similarity index 100%
rename from docs/assets/guides/stage/ablation-se-ratio.html
rename to astro-site/public/assets/guides/stage/ablation-se-ratio.html
diff --git a/docs/assets/guides/stage/ablation-temporal.html b/astro-site/public/assets/guides/stage/ablation-temporal.html
similarity index 100%
rename from docs/assets/guides/stage/ablation-temporal.html
rename to astro-site/public/assets/guides/stage/ablation-temporal.html
diff --git a/docs/assets/guides/stage/ablation-width.html b/astro-site/public/assets/guides/stage/ablation-width.html
similarity index 100%
rename from docs/assets/guides/stage/ablation-width.html
rename to astro-site/public/assets/guides/stage/ablation-width.html
diff --git a/docs/assets/guides/stage/sleep-cycle-pie.html b/astro-site/public/assets/guides/stage/sleep-cycle-pie.html
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rename from docs/assets/guides/stage/sleep-cycle-pie.html
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diff --git a/docs/assets/logo-white.png b/astro-site/public/assets/logo-white.png
similarity index 100%
rename from docs/assets/logo-white.png
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diff --git a/docs/assets/logo.png b/astro-site/public/assets/logo.png
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rename from docs/assets/logo.png
rename to astro-site/public/assets/logo.png
diff --git a/docs/assets/sleepkit-banner.png b/astro-site/public/assets/sleepkit-banner.png
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rename from docs/assets/sleepkit-banner.png
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rename to astro-site/public/assets/sleepkit-logo-light.png
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rename from docs/assets/tasks/detect/ambiq-watch.webp
rename to astro-site/public/assets/tasks/detect/ambiq-watch.webp
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rename from docs/assets/tasks/detect/demo-sleep-cycle-pie.html
rename to astro-site/public/assets/tasks/detect/demo-sleep-cycle-pie.html
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rename to astro-site/public/assets/tasks/sleepkit-task-diagram.svg
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rename to astro-site/public/assets/tasks/stage/sleep-stage.svg
diff --git a/docs/assets/tcn.svg b/astro-site/public/assets/tcn.svg
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diff --git a/astro-site/scripts/build-content.mjs b/astro-site/scripts/build-content.mjs
deleted file mode 100644
index 21e8fe3..0000000
--- a/astro-site/scripts/build-content.mjs
+++ /dev/null
@@ -1,175 +0,0 @@
-import { internalPages } from "./public-docs.mjs";
-import {
-  existsSync,
-  readFileSync,
-  writeFileSync,
-  mkdirSync,
-  readdirSync,
-  cpSync,
-  rmSync,
-} from "node:fs";
-import { dirname, resolve, relative } from "node:path";
-import {
-  convertPage,
-  createState,
-  parseNav,
-  buildSidebar,
-} from "../node_modules/@ambiqai/helia-ui/scripts/lib/mkdocs-convert-render.mjs";
-import { normalizeMarkdown, expandSnippets } from "./normalize-markdown.mjs";
-const source = resolve("../docs");
-const out = resolve("src/content/docs");
-const walk = (dir) =>
-  readdirSync(dir, { withFileTypes: true }).flatMap((e) =>
-    e.isDirectory() ? walk(resolve(dir, e.name)) : [resolve(dir, e.name)],
-  );
-const state = createState();
-const titles = {};
-rmSync(out, { recursive: true, force: true });
-mkdirSync(out, { recursive: true });
-rmSync("public/examples", { recursive: true, force: true });
-mkdirSync("src/data", { recursive: true });
-function readSnippet(file) {
-  const path = resolve(source, file);
-  if (!path.startsWith(source + "/"))
-    throw Error(`Snippet outside docs: ${file}`);
-  if (!existsSync(path)) {
-    if (file !== "assets/segmentation_example.html")
-      throw Error(`Missing snippet: ${file}`);
-    state.handPass.push(`Missing source snippet: ${file}`);
-    return "> The source repository does not include this example asset.";
-  }
-  if (file.endsWith(".html"))
-    return `<iframe title="Interactive example" src="/sleepkit/${file}" loading="lazy" style={{width:'100%',height:'480px',border:0}} />`;
-  return readFileSync(path, "utf8");
-}
-for (const file of walk(source)) {
-  const rel = relative(source, file);
-  if (/^(css|js|overrides)\//.test(rel) || internalPages.has(rel)) continue;
-  if (!rel.endsWith(".md")) {
-    if (!rel.endsWith(".ipynb")) {
-      mkdirSync(dirname("public/" + rel), { recursive: true });
-      cpSync(file, "public/" + rel);
-    }
-    continue;
-  }
-  if (rel.startsWith("assets/")) continue;
-  let raw = expandSnippets(
-    readFileSync(file, "utf8").replace(/<!--[\s\S]*?-->/g, ""),
-    readSnippet,
-  );
-  raw = raw.replace(/\[([^\]]+)\]\(([^)]+)\)/g, (all, label, href) => {
-    if (/^(https?:|mailto:|#|\/)/.test(href)) return all;
-    const target = relative(source, resolve(dirname(file), href.split("#")[0]));
-    return internalPages.has(target) ? label : all;
-  });
-  raw = raw.replace(/\]\(([^)]+)\)/g, (all, href) => {
-    if (/^(https?:|mailto:|#|\/)/.test(href)) return all;
-    const [path, hash] = href.split("#");
-    const target = relative(
-      source,
-      resolve(dirname(file), path.replace(/\.md\/$/, ".md")),
-    );
-    if (target.startsWith("../"))
-      return `](https://github.com/AmbiqAI/sleepkit/blob/main/${relative(resolve(".."), resolve(dirname(file), path))}${hash ? "#" + hash : ""})`;
-    const route = target.replace(/(?:index)?\.md$/, "").replace(/\.ipynb$/, "");
-    const suffix =
-      /\.(md|ipynb)\/?$/.test(path) && route && !route.endsWith("/") ? "/" : "";
-    return `](/sleepkit/${route}${suffix}${hash ? "#" + hash : ""})`;
-  });
-  raw = raw.replace(/^#\s*$/m, "# sleepKIT");
-  raw = raw.replace(/:(?:material|simple|fontawesome|octicons)-[\w-]+:/g, "");
-  raw = raw.replace(/\]\(([^)]+)\.ipynb\)/g, "]($1/)");
-  raw = raw.replace(
-    /\[([^\]]+)\]\(([^)]+)\)\{\s*\.md-button\s*\}/g,
-    '<a className="md-button" href="$2">$1</a>',
-  );
-  const intro = rel === "index.md" ? raw.match(/<div class="sleepkit-intro">\s*([\s\S]*?)\s*<\/div>/)?.[1].trim() : undefined;
-  if (intro) raw = raw.replace(/<div class="sleepkit-intro">[\s\S]*?<\/div>/, "");
-  raw = normalizeMarkdown(raw);
-  const page = convertPage(raw, rel, { state, base: "/sleepkit" });
-  let text = page.text
-    .replace(/#only-light/g, "#only-light")
-    .replace(/<br\s*>/g, "<br />");
-  if (rel === "index.md")
-    text = text.replace(
-      /^description:.*$/m,
-      'description: \"AI development kit for sleep monitoring on Ambiq devices.\"',
-    );
-  if (intro) text = text.replace(/^description:.*$/m, (line) => `${line}\nhero:\n  tagline: ${JSON.stringify(intro)}`);
-  let blockIndex = 0;
-  text = text.replace(
-    /^([ \t]*)(`{3,})([\w+-]+)([^\n]*)\n([\s\S]*?)^\1\2[ \t]*$/gm,
-    (all, indent, fence, lang, meta, body) => {
-      if (["mermaid", "text"].includes(lang)) return all;
-      const extension =
-        {
-          python: "py",
-          py: "py",
-          bash: "sh",
-          sh: "sh",
-          console: "sh",
-          json: "json",
-          jsonc: "jsonc",
-          yaml: "yaml",
-          yml: "yaml",
-        }[lang] || lang;
-      const code = body
-        .split("\n")
-        .map((l) => (l.startsWith(indent) ? l.slice(indent.length) : l))
-        .join("\n")
-        .trimEnd();
-      if (lang === "json") {
-        try {
-          JSON.parse(code);
-        } catch (error) {
-          throw new Error(`Invalid JSON example in ${rel}: ${error.message}`);
-        }
-      }
-      if (meta.includes("fragment"))
-        return `${indent}${fence}${lang} title="Configuration fragment"\n${body}${indent}${fence}`;
-      const lines = code.split("\n").length;
-      const name = `${rel.replace(/\.md$/, "").replaceAll("/", "-")}-${++blockIndex}.${extension}`;
-      if (
-        (["json", "jsonc", "yaml", "yml"].includes(lang) && lines > 16) ||
-        (["py", "python"].includes(lang) && lines > 45)
-      ) {
-        mkdirSync("public/examples", { recursive: true });
-        writeFileSync("public/examples/" + name, code + "\n");
-        return `${indent}<ConfigExample code={${JSON.stringify(code)}} lang=${JSON.stringify(lang === "py" ? "python" : lang)} filename=${JSON.stringify(name)} download=${JSON.stringify("/sleepkit/examples/" + name)}>\n\n${indent}${fence}${lang} title="${name}"\n${body}${indent}${fence}\n\n${indent}</ConfigExample>`;
-      }
-      const title =
-        lang === "py" || lang === "python"
-          ? "Python example"
-          : ["bash", "sh", "console"].includes(lang)
-            ? "Terminal"
-            : lang.toUpperCase() + " example";
-      return `${indent}${fence}${lang}${meta.includes("title=") ? meta : ' title="' + title + '"' + meta}\n${body}${indent}${fence}`;
-    },
-  );
-  if (text.includes("<ConfigExample "))
-    text = text.replace(
-      /(---\n[\s\S]*?\n---)/,
-      '$1\n\nimport ConfigExample from "' +
-        relative(
-          dirname(resolve(out, rel)),
-          resolve("src/components/ConfigExample.astro"),
-        ).replaceAll("\\", "/") +
-        '";',
-    );
-  const dest = resolve(out, rel.replace(/\.md$/, ".mdx"));
-  mkdirSync(dirname(dest), { recursive: true });
-  writeFileSync(dest, text);
-  titles[rel] = page.title;
-  for (const warning of page.handPass)
-    state.handPass.push(`${rel}: ${warning}`);
-}
-const nav = parseNav(readFileSync("../mkdocs.yml", "utf8"));
-writeFileSync(
-  "src/data/sidebar.json",
-  JSON.stringify(buildSidebar(nav, titles), null, 2),
-);
-mkdirSync(".cache", { recursive: true });
-writeFileSync(".cache/conversion.json", JSON.stringify(state, null, 2));
-console.log(
-  `Converted ${state.counts.pages} authored pages; manual checks recorded in .cache/conversion.json.`,
-);
diff --git a/astro-site/scripts/build-examples.mjs b/astro-site/scripts/build-examples.mjs
new file mode 100644
index 0000000..f477b6e
--- /dev/null
+++ b/astro-site/scripts/build-examples.mjs
@@ -0,0 +1,25 @@
+import { cpSync, mkdirSync, readFileSync, readdirSync, rmSync, writeFileSync } from "node:fs";
+import { join, basename } from "node:path";
+
+const walk = (dir) => readdirSync(dir, { withFileTypes: true }).flatMap((entry) =>
+  entry.isDirectory() ? walk(join(dir, entry.name)) : [join(dir, entry.name)],
+);
+rmSync("public/examples", { recursive: true, force: true });
+mkdirSync("public/examples", { recursive: true });
+const downloads = new Map();
+for (const file of walk("src/content/docs").filter((path) => path.endsWith(".mdx"))) {
+  const source = readFileSync(file, "utf8");
+  for (const match of source.matchAll(/<ConfigExample code=\{("(?:[^"\\]|\\.)*")\} lang="[^"]+" filename=("(?:[^"\\]|\\.)*")/g)) {
+    const code = JSON.parse(match[1]);
+    const name = JSON.parse(match[2]);
+    if (basename(name) !== name) throw new Error(`Invalid example filename in ${file}`);
+    if (downloads.has(name) && downloads.get(name) !== code) throw new Error(`Conflicting example: ${name}`);
+    if (name.endsWith(".json")) JSON.parse(code);
+    downloads.set(name, code);
+  }
+}
+for (const [name, code] of downloads) writeFileSync(join("public/examples", name), code + "\n");
+console.log(`Generated ${downloads.size} downloads from authored examples.`);
+
+rmSync("public/evidence", { recursive: true, force: true });
+cpSync("../docs/evidence", "public/evidence", { recursive: true });
diff --git a/astro-site/scripts/build-notebooks.py b/astro-site/scripts/build-notebooks.py
index 1732c77..ee6a6a1 100644
--- a/astro-site/scripts/build-notebooks.py
+++ b/astro-site/scripts/build-notebooks.py
@@ -12,7 +12,7 @@
 shutil.rmtree(assets, ignore_errors=True)
 assets.mkdir(parents=True, exist_ok=True)
 shutil.copyfile(source, assets / source.name)
-shutil.copyfile(Path("../docs/guides/train-detect-model.ipynb"), assets / "previous-docs-train-detect-model.ipynb")
+shutil.copyfile(Path("../notebooks/archive/previous-docs-train-detect-model.ipynb"), assets / "previous-docs-train-detect-model.ipynb")
 parts = [
     "---\ntitle: Train Sleep Detection Model\ndescription: Train a wrist-based sleep detection model with saved notebook code and outputs.\n---",
     f'<div class="sleepkit-actions"><a class="md-button" href="/sleepkit/notebooks/{source.name}">Download notebook</a> <a class="md-button" href="https://github.com/AmbiqAI/sleepkit/blob/main/notebooks/{source.name}">View source</a> <a class="md-button" href="https://colab.research.google.com/github/AmbiqAI/sleepkit/blob/main/notebooks/{source.name}">Open in Colab</a></div>',
diff --git a/astro-site/scripts/build-reference.mjs b/astro-site/scripts/build-reference.mjs
index 43a2de7..148e6b7 100644
--- a/astro-site/scripts/build-reference.mjs
+++ b/astro-site/scripts/build-reference.mjs
@@ -12,6 +12,8 @@ const run = (cmd, args) =>
 rmSync("public/reference", { recursive: true, force: true });
 const commit = run("git", ["rev-parse", "HEAD"]).trim();
 mkdirSync(".cache", { recursive: true });
+mkdirSync("src/data", { recursive: true });
+rmSync("src/content/docs/reference/api", { recursive: true, force: true });
 const dump = JSON.parse(
   run("uv", [
     "tool",
@@ -107,14 +109,13 @@ writeFileSync(
 );
 writeFileSync(
   "src/data/redirects.json",
-  JSON.stringify(
-    Object.fromEntries(
-      modules.map((m) => [
-        "/api/" + m.path.replaceAll(".", "/"),
-        "/sleepkit/" + route(m) + "/",
-      ]),
-    ),
-  ),
+  JSON.stringify({
+    ...JSON.parse(readFileSync("src/redirects.json", "utf8")),
+    ...Object.fromEntries(modules.map((m) => [
+      "/api/" + m.path.replaceAll(".", "/"),
+      "/sleepkit/" + route(m) + "/",
+    ])),
+  }),
 );
 writeFileSync(
   "src/content/docs/reference/index.mdx",
diff --git a/astro-site/scripts/canonical-content.test.mjs b/astro-site/scripts/canonical-content.test.mjs
new file mode 100644
index 0000000..27f7834
--- /dev/null
+++ b/astro-site/scripts/canonical-content.test.mjs
@@ -0,0 +1,21 @@
+import assert from "node:assert/strict";
+import { execFileSync } from "node:child_process";
+import { readFileSync, readdirSync } from "node:fs";
+import { join, relative } from "node:path";
+import test from "node:test";
+
+const walk = (dir) => readdirSync(dir, { withFileTypes: true }).flatMap((entry) =>
+  entry.isDirectory() ? walk(join(dir, entry.name)) : [join(dir, entry.name)],
+);
+const snapshot = () => Object.fromEntries([
+  ...walk("src/content/docs").filter((p) => p.endsWith(".mdx") && !p.startsWith("src/content/docs/reference/")),
+  "src/navigation.mjs", "src/redirects.json",
+  ...walk("public").filter((p) => !/^public\/(reference|notebooks|examples)\//.test(p)),
+].map((p) => [relative(".", p), readFileSync(p).toString("base64")]));
+
+test("generation preserves authored pages, navigation, redirects and static assets", () => {
+  const before = snapshot();
+  assert(Object.keys(before).length > 40, "Missing canonical content");
+  execFileSync("npm", ["run", "prepare:docs"], { stdio: "pipe" });
+  assert.deepEqual(snapshot(), before);
+});
diff --git a/astro-site/scripts/check-doc-examples.py b/astro-site/scripts/check-doc-examples.py
index 217871d..1ea0cc8 100644
--- a/astro-site/scripts/check-doc-examples.py
+++ b/astro-site/scripts/check-doc-examples.py
@@ -10,7 +10,7 @@
     tree = ast.parse((root / "sleepkit/features" / f"{name}.py").read_text())
     function = next(n for n in ast.walk(tree) if isinstance(n, ast.FunctionDef) and n.name == "feature_names")
     names = ast.literal_eval(next(n.value for n in ast.walk(function) if isinstance(n, ast.Return)))
-    doc = (root / "docs/features" / f"{name}.md").read_text()
+    doc = (root / "astro-site/src/content/docs/features" / f"{name}.mdx").read_text()
     table = re.findall(r"^\| (\w+) \|", doc, re.MULTILINE)
     assert table == names, f"{name}: feature table does not match feature_names()"
     paths = {
@@ -33,7 +33,7 @@
     "modes/demo.md",
     "datasets/cmidss.md",
 ]:
-    text = (root / "docs" / rel).read_text()
+    text = (root / "astro-site/src/content/docs" / (rel + "x")).read_text()
     for match in re.finditer(r"^([ \t]*)```(?:py|python)(?:[^\n]*)\n(.*?)^\1```", text, re.MULTILINE | re.DOTALL):
         ast.parse(textwrap.dedent(match[2]))
 print("Verified feature tables, HDF5 paths and updated Python example syntax.")
diff --git a/astro-site/scripts/check-output.mjs b/astro-site/scripts/check-output.mjs
index 563a0f0..fed7452 100644
--- a/astro-site/scripts/check-output.mjs
+++ b/astro-site/scripts/check-output.mjs
@@ -71,18 +71,13 @@ console.log(
   `Verified internal links across ${cache.size} pages, ${index.rows.length} API symbols and discovery exports.`,
 );
 
-for (const path of files(resolve("../docs")).filter(
-  (p) => p.endsWith(".md") && !p.includes("/assets/"),
-)) {
-  const rel = relative(resolve("../docs"), path).replace(/(?:index)?\.md$/, "");
-  if (internalPages.has(relative(resolve("../docs"), path))) {
-    assert(!existsSync(join(root, rel, "index.html")), `Internal page published: ${rel}`);
-    continue;
-  }
-  assert(
-    existsSync(join(root, rel, "index.html")),
-    `Missing authored route: ${rel}`,
-  );
+for (const path of files(resolve("src/content/docs")).filter((p) => /\.mdx?$/.test(p))) {
+  const rel = relative(resolve("src/content/docs"), path).replace(/(?:index)?\.mdx?$/, "");
+  assert(existsSync(join(root, rel, "index.html")), `Missing authored route: ${rel}`);
+}
+for (const path of internalPages) {
+  const rel = path.replace(/(?:index)?\.md$/, "");
+  assert(!existsSync(join(root, rel, "index.html")), `Internal page published: ${rel}`);
 }
 const notebook = JSON.parse(
   readFileSync("../notebooks/train-detect-model.ipynb", "utf8"),
@@ -202,7 +197,7 @@ assert(
   "Notebook duplicates the page title",
 );
 assert.deepEqual(
-  readFileSync("../docs/guides/train-detect-model.ipynb"),
+  readFileSync("../notebooks/archive/previous-docs-train-detect-model.ipynb"),
   readFileSync("dist/notebooks/previous-docs-train-detect-model.ipynb"),
 );
 assert(
diff --git a/astro-site/scripts/normalize-markdown.mjs b/astro-site/scripts/normalize-markdown.mjs
deleted file mode 100644
index 98605fe..0000000
--- a/astro-site/scripts/normalize-markdown.mjs
+++ /dev/null
@@ -1,90 +0,0 @@
-/** Normalize Material block syntax before the shared MDX converter runs. */
-export function normalizeMarkdown(text) {
-  const lines = text.split("\n");
-  function blocks(input) {
-    const out = [];
-    let fence;
-    for (let i = 0; i < input.length; i++) {
-      const line = input[i];
-      const token = line.match(/^\s*(`{3,}|~{3,})/);
-      if (token) {
-        if (!fence) fence = token[1];
-        else if (token[1].startsWith(fence)) fence = undefined;
-        out.push(line);
-        continue;
-      }
-      if (fence) {
-        out.push(line);
-        continue;
-      }
-      const match = line.match(
-        /^([ \t]*)(!!!|\?\?\?\+?)\s+([\w-]+)(?:\s+(.*))?$/,
-      );
-      if (match) {
-        const [, indent, marker, rawType, rawTitle = ""] = match;
-        const type = rawType.toLowerCase();
-        const title = rawTitle.replace(/^["']|["']$/g, "");
-        const body = [];
-        let j = i + 1;
-        while (
-          j < input.length &&
-          (!input[j].trim() || input[j].startsWith(indent + "    "))
-        ) {
-          body.push(input[j].trim() ? input[j].slice(indent.length + 4) : "");
-          j++;
-        }
-        const inner = blocks(body).join("\n").trim();
-        if (marker.startsWith("???")) {
-          const label = title || type[0].toUpperCase() + type.slice(1);
-          out.push(
-            "",
-            `${indent}<details${marker.endsWith("+") ? " open" : ""}>`,
-            `${indent}<summary>${label}</summary>`,
-            "",
-            ...inner.split("\n").map((l) => indent + l),
-            "",
-            `${indent}</details>`,
-            "",
-          );
-        } else if (["example", "install"].includes(type)) {
-          if (title) out.push("", `${indent}**${title}**`, "");
-          out.push(...inner.split("\n").map((l) => indent + l), "");
-        } else {
-          out.push(
-            `${indent}!!! ${type}${title ? " " + JSON.stringify(title) : ""}`,
-            "",
-            ...inner.split("\n").map((l) => indent + "    " + l),
-            "",
-          );
-        }
-        i = j - 1;
-        continue;
-      }
-      out.push(
-        line.replace(
-          /(!\[[^\]]*\]\([^)]*\))\{\s*width=["']?(\d+)["']?\s*\}/g,
-          (_, image, width) => {
-            const [, alt, src] = image.match(/!\[([^\]]*)\]\(([^)]*)\)/);
-            return `<img src="${src}" alt="${alt}" width="${width}" />`;
-          },
-        ),
-      );
-    }
-    return out;
-  }
-  return blocks(lines).join("\n");
-}
-
-/** Keep snippet indentation local to the directive's line. */
-export function expandSnippets(text, read, seen = []) {
-  return text.replace(
-    /^([ \t]*)--8<--[ \t]+"([^"]+)"[ \t]*$/gm,
-    (_, indent, file) => {
-      if (seen.includes(file)) throw Error(`Cyclic snippet: ${file}`);
-      return expandSnippets(read(file), read, [...seen, file])
-        .split("\n")
-        .map((line) => (line ? indent + line : ""))
-        .join("\n");
-    },
-  );
-}
diff --git a/astro-site/scripts/normalize-markdown.test.mjs b/astro-site/scripts/normalize-markdown.test.mjs
deleted file mode 100644
index ab1c92f..0000000
--- a/astro-site/scripts/normalize-markdown.test.mjs
+++ /dev/null
@@ -1,64 +0,0 @@
-import { test } from "node:test";
-import assert from "node:assert/strict";
-import { normalizeMarkdown, expandSnippets } from "./normalize-markdown.mjs";
-import {
-  convertPage,
-  createState,
-} from "../node_modules/@ambiqai/helia-ui/scripts/lib/mkdocs-convert-render.mjs";
-const convert = (raw) =>
-  convertPage(normalizeMarkdown(raw), "example.md", {
-    state: createState(),
-    base: "/sleepkit",
-  }).text;
-test("snippet expansion keeps table rows adjacent and preserves surrounding blank lines", () => {
-  const table = "| Class | Label |\n| --- | --- |\n| 0 | Wake |";
-  assert.equal(
-    expandSnippets('Before\n\n--8<-- "table.md"\n\nAfter', () => table),
-    `Before\n\n${table}\n\nAfter`,
-  );
-  assert.equal(
-    expandSnippets('    --8<-- "table.md"', () => table),
-    table
-      .split("\n")
-      .map((l) => "    " + l)
-      .join("\n"),
-  );
-  assert.throws(
-    () => expandSnippets('--8<-- "loop"', () => '--8<-- "loop"'),
-    /Cyclic/,
-  );
-});
-test("mixed-case examples and installation wrappers become ordinary content", () => {
-  for (const type of ["Example", "example", "install"]) {
-    const result = convert(
-      `# Test\n\n!!! ${type} "Example title"\n\n    === "Python"\n\n        Useful example.`,
-    );
-    assert(!result.includes("!!!"));
-    assert(!result.includes(":::note"));
-    assert(result.includes("<Tabs>"));
-    assert(result.includes("**Example title**"));
-  }
-});
-test("actual notes remain callouts and collapsed admonitions retain open state", () => {
-  assert(
-    convert('# Test\n\n!!! Note "Important"\n\n    Keep this note.').includes(
-      ":::note[Important]",
-    ),
-  );
-  const closed = convert(
-    '# Test\n\n??? Example "Advanced"\n\n    Hidden detail.',
-  );
-  assert(closed.includes("<details>"));
-  assert(closed.includes("<summary>Advanced</summary>"));
-  assert(
-    convert('# Test\n\n???+ note "Expanded"\n\n    Detail.').includes(
-      "<details open>",
-    ),
-  );
-});
-test("image attributes become image properties; code samples remain verbatim", () => {
-  const image = normalizeMarkdown('![Watch](/watch.webp){ width="540" }');
-  assert.equal(image, '<img src="/watch.webp" alt="Watch" width="540" />');
-  const fenced = '```text\n!!! Example\n{ width="540" }\n```';
-  assert.equal(normalizeMarkdown(fenced), fenced);
-});
diff --git a/docs/datasets/byod.md b/astro-site/src/content/docs/datasets/byod.mdx
similarity index 87%
rename from docs/datasets/byod.md
rename to astro-site/src/content/docs/datasets/byod.mdx
index 0edc66e..0525a8c 100644
--- a/docs/datasets/byod.md
+++ b/astro-site/src/content/docs/datasets/byod.mdx
@@ -1,12 +1,15 @@
-# Bring-Your-Own-Dataset (BYOD)
+---
+title: "Bring-Your-Own-Dataset (BYOD)"
+description: "A custom dataset subclasses sk.Dataset, exposes subject IDs, and supplies the signal and label readers consumed by its feature extractor. There is no universal…"
+---
 
-A custom dataset subclasses `sk.Dataset`, exposes subject IDs, and supplies the signal and label readers consumed by its feature extractor. There is no universal signal schema: pair the adapter with a [custom feature set](../features/byofs.md).
+A custom dataset subclasses `sk.Dataset`, exposes subject IDs, and supplies the signal and label readers consumed by its feature extractor. There is no universal signal schema: pair the adapter with a [custom feature set](/sleepkit/features/byofs/).
 
 ## Define the adapter
 
 This example reads an existing directory of subject `.npz` files with a `signal` array. It makes an explicit subject split and does not download or invent data.
 
-```python
+```python title="Python example"
 from pathlib import Path
 import random
 import numpy as np
diff --git a/docs/datasets/cmidss.md b/astro-site/src/content/docs/datasets/cmidss.mdx
similarity index 80%
rename from docs/datasets/cmidss.md
rename to astro-site/src/content/docs/datasets/cmidss.mdx
index 41ff5d6..7c38c73 100644
--- a/docs/datasets/cmidss.md
+++ b/astro-site/src/content/docs/datasets/cmidss.mdx
@@ -1,4 +1,7 @@
-# CMIDSS Dataset
+---
+title: "CMIDSS Dataset"
+description: "This dataset comes from the Child Mind Institute - Detect Sleep States (CMIDSS) Kaggle competition. The dataset comprises 300 subjects with over 500 multi-day…"
+---
 
 ## Overview
 
@@ -16,13 +19,13 @@ The CMIDSS dataset is available for non-commercial use under [Attribution-NonCom
 
 ## Task integration
 
-Use CMIDSS for [sleep detection](../tasks/detect.md). Its onset/wakeup annotations do not provide sleep-stage or apnea labels. See the [detection label policy](../detection-label-policy.md) for the derived target used by the experimental recipe.
+Use CMIDSS for [sleep detection](/sleepkit/tasks/detect/). Its onset/wakeup annotations do not provide sleep-stage or apnea labels. See the [detection label policy](/sleepkit/detection-label-policy/) for the derived target used by the experimental recipe.
 
 ## Installation
 
 The dataset adapter has two download paths. `download_raw_dataset()` retrieves the competition data through the Kaggle client and converts subject recordings to HDF5. Configure the Kaggle client and obtain competition access before running:
 
-```python
+```python title="Python example"
 import sleepkit as sk
 
 dataset = sk.DatasetFactory.get("cmidss")(path="./datasets/cmidss")
diff --git a/astro-site/src/content/docs/datasets/index.mdx b/astro-site/src/content/docs/datasets/index.mdx
new file mode 100644
index 0000000..6788e23
--- /dev/null
+++ b/astro-site/src/content/docs/datasets/index.mdx
@@ -0,0 +1,34 @@
+---
+title: "Datasets"
+description: "sleepKIT provides support for a number of datasets to facilitate training the sleep-monitoring tasks. Most of the datasets are readily available and can be…"
+---
+
+sleepKIT provides support for a number of datasets to facilitate training the __sleep-monitoring tasks__. Most of the datasets are readily available and can be downloaded and used for training and evaluation. The datasets inherit from [Dataset](/sleepkit/api/sleepkit/datasets/dataset) and can be accessed either directly or through the factory singleton [`sk.DatasetFactory`](#dataset-factory).
+
+## Available Datasets
+
+Below is a list of the currently available datasets in sleepKIT. Please make sure to review each dataset's license for terms and limitations.
+
+* **[MESA](/sleepkit/datasets/mesa/)**: A longitudinal investigation of factors associated with the development of subclinical cardiovascular disease and the progression of subclinical to clinical cardiovascular disease in 6,814 black, white, Hispanic, and Chinese
+
+* **[CMIDSS](/sleepkit/datasets/cmidss/)**: The Child Mind Institute - Detect Sleep States (CMIDSS) dataset comprises 300 subjects with over 500 multi-day recordings of wrist-worn accelerometer data annotated with two event types: onset, the beginning of sleep, and wakeup, the end of sleep.
+
+* **[YSYW](/sleepkit/datasets/ysyw/)**: A total of 1,983 PSG recordings were provided by the Massachusetts General Hospital’s (MGH) Sleep Lab in the Sleep Division together with the Computational Clinical Neurophysiology Laboratory, and the Clinical Data Ani- mation Center.
+
+* **[STAGES](/sleepkit/datasets/stages/)**: The Stanford Technology Analytics and Genomics in Sleep (STAGES) study is a prospective cross-sectional, multi-site study involving 20 data collection sites from six centers including Stanford University, Bogan Sleep Consulting, Geisinger Health, Mayo Clinic, MedSleep, and St. Luke's Hospital.
+
+* **[Bring-Your-Own-Data](/sleepkit/datasets/byod/)**: Add new datasets to sleepKIT by providing your own data. Subclass `Dataset` and register it with the `sk.DatasetFactory`.
+
+---
+
+## Dataset Factory
+
+The dataset factory, `sk.DatasetFactory`, provides a convenient way to access the datasets. The factory is a thread-safe singleton class that provides a single point of access to the datasets via the datasets' slug names. The benefit of using the factory is it allows registering new additional datasets that can then be leveraged by existing and new tasks.
+
+The dataset factory provides the following methods:
+
+* **sk.DatasetFactory.register**: Register a custom dataset
+* **sk.DatasetFactory.unregister**: Unregister a custom dataset
+* **sk.DatasetFactory.has**: Check if a dataset is registered
+* **sk.DatasetFactory.get**: Get a dataset
+* **sk.DatasetFactory.list**: List all available datasets
diff --git a/docs/datasets/mesa.md b/astro-site/src/content/docs/datasets/mesa.mdx
similarity index 86%
rename from docs/datasets/mesa.md
rename to astro-site/src/content/docs/datasets/mesa.mdx
index 0a3b112..63fe1a3 100644
--- a/docs/datasets/mesa.md
+++ b/astro-site/src/content/docs/datasets/mesa.mdx
@@ -1,4 +1,7 @@
-# MESA Dataset
+---
+title: "MESA Dataset"
+description: "Multi-Ethnic Study of Atherosclerosis (MESA) is an NHLBI-sponsored 6-center collaborative longitudinal investigation of factors associated with the development…"
+---
 
 ## Overview
 
@@ -16,15 +19,15 @@ The MESA dataset is available for non-commercial and commercial use.
 
 ## Supported Tasks
 
-* [Sleep Detect](../tasks/detect.md)
-* [Sleep Stage](../tasks/stage.md)
-* [Sleep Apnea](../tasks/apnea.md)
+* [Sleep Detect](/sleepkit/tasks/detect/)
+* [Sleep Stage](/sleepkit/tasks/stage/)
+* [Sleep Apnea](/sleepkit/tasks/apnea/)
 
 ## Installation
 
 The MESA dataset is available for download from the [NSRR website](https://sleepdata.org/datasets/mesa). Please note, a user account and permission to access the dataset is required. Once granted permission, the dataset can be downloaded using the `sleepkit` package. Please note, the `NSRR_TOKEN` environment variable must be set to the user's token prior to downloading. The dataset can be downloaded using the following command:
 
-```bash
+```bash title="Terminal"
 
 export NSRR_TOKEN="INSERT_TOKEN_HERE"
 
diff --git a/docs/datasets/stages.md b/astro-site/src/content/docs/datasets/stages.mdx
similarity index 89%
rename from docs/datasets/stages.md
rename to astro-site/src/content/docs/datasets/stages.mdx
index 577b5c9..a90a6b7 100644
--- a/docs/datasets/stages.md
+++ b/astro-site/src/content/docs/datasets/stages.mdx
@@ -1,4 +1,7 @@
-# STAGES Dataset
+---
+title: "STAGES Dataset"
+description: "The Stanford Technology Analytics and Genomics in Sleep (STAGES) study is a prospective cross-sectional, multi-site study involving 20 data collection sites…"
+---
 
 ## Overview
 
@@ -29,15 +32,15 @@ The STAGES dataset is available for non-commercial and commercial use.
 
 ## Supported Tasks
 
-* [Sleep Detect](../tasks/detect.md)
-* [Sleep Stage](../tasks/stage.md)
-* [Sleep Apnea](../tasks/apnea.md)
+* [Sleep Detect](/sleepkit/tasks/detect/)
+* [Sleep Stage](/sleepkit/tasks/stage/)
+* [Sleep Apnea](/sleepkit/tasks/apnea/)
 
 ## Installation
 
 The STAGES dataset is available for download from the [NSRR website](https://sleepdata.org/datasets/stages). Please note, a user account and permission to access the dataset is required. Once granted permission, the dataset can be downloaded using the `sleepkit` package. Please note, the `NSRR_TOKEN` environment variable must be set to the user's token prior to downloading. The dataset can be downloaded using the following command:
 
-```bash
+```bash title="Terminal"
 
 export NSRR_TOKEN="INSERT_TOKEN_HERE"
 
diff --git a/docs/datasets/synthetic.md b/astro-site/src/content/docs/datasets/synthetic.mdx
similarity index 50%
rename from docs/datasets/synthetic.md
rename to astro-site/src/content/docs/datasets/synthetic.mdx
index 2ce9262..8a0cbde 100644
--- a/docs/datasets/synthetic.md
+++ b/astro-site/src/content/docs/datasets/synthetic.mdx
@@ -1,4 +1,7 @@
-# Synthetic Data
+---
+title: "Synthetic Data"
+description: "By leveraging physioKIT, we are able to generate synthetic data for a variety of physiological signals, including ECG, PPG, and respiration. In addition to the…"
+---
 
 ### Overview
 
@@ -6,7 +9,6 @@ By leveraging [physioKIT](https://ambiqai.github.io/physiokit/), we are able to
 
 Please visit [physioKIT](https://ambiqai.github.io/physiokit/) for more details.
 
-
 ## Funding
 
 NA
@@ -17,31 +19,29 @@ The tool is available under BSD-3-Clause License.
 
 ## Integration
 
-
-
 ## Usage
 
-!!! Example Python
+**Python**
 
-    ```py linenums="1"
-    import physiokit as pk
+```py title="Python example" linenums="1"
+import physiokit as pk
 
-    heart_rate = 64 # BPM
-    sample_rate = 1000 # Hz
-    signal_length = 10*sample_rate # 10 seconds
+heart_rate = 64 # BPM
+sample_rate = 1000 # Hz
+signal_length = 10*sample_rate # 10 seconds
 
-    # Generate NSR synthetic ECG signal
-    ecg, segs, fids = pk.ecg.synthesize(
-        signal_length=signal_length,
-        sample_rate=sample_rate,
-        heart_rate=heart_rate,
-        leads=1,
-        preset=pk.ecg.EcgPreset.NSR,
-        p_multiplier=1.5,
-        t_multiplier=1.2,
-        noise_multiplier=0.2
-    )
+# Generate NSR synthetic ECG signal
+ecg, segs, fids = pk.ecg.synthesize(
+    signal_length=signal_length,
+    sample_rate=sample_rate,
+    heart_rate=heart_rate,
+    leads=1,
+    preset=pk.ecg.EcgPreset.NSR,
+    p_multiplier=1.5,
+    t_multiplier=1.2,
+    noise_multiplier=0.2
+)
 
-    ```
+```
 
-Synthetic signal generation is a standalone preprocessing tool, not a registered sleepKIT dataset. To use generated signals in a task, provide a [custom dataset adapter](byod.md) and matching feature extraction.
+Synthetic signal generation is a standalone preprocessing tool, not a registered sleepKIT dataset. To use generated signals in a task, provide a [custom dataset adapter](/sleepkit/datasets/byod/) and matching feature extraction.
diff --git a/docs/datasets/ysyw.md b/astro-site/src/content/docs/datasets/ysyw.mdx
similarity index 73%
rename from docs/datasets/ysyw.md
rename to astro-site/src/content/docs/datasets/ysyw.mdx
index a54d4e2..96697db 100644
--- a/docs/datasets/ysyw.md
+++ b/astro-site/src/content/docs/datasets/ysyw.mdx
@@ -1,4 +1,7 @@
-# YSYW Dataset
+---
+title: "YSYW Dataset"
+description: "A total of 1,983 PSG recordings were provided by the Massachusetts General Hospital’s (MGH) Sleep Lab in the Sleep Division together with the Computational…"
+---
 
 ## Overview
 
@@ -14,9 +17,8 @@ Funding was from the National Institutes of Health, grant R01-GM104987. We are a
 
 The YSYW dataset is available commercial use under [Open Data Commons Attribution License](https://physionet.org/content/challenge-2018/view-license/1.0.0/).
 
-
 ## Supported Tasks
 
-* [Sleep Detect](../tasks/detect.md)
-* [Sleep Stage](../tasks/stage.md)
-* [Sleep Apnea](../tasks/apnea.md)
+* [Sleep Detect](/sleepkit/tasks/detect/)
+* [Sleep Stage](/sleepkit/tasks/stage/)
+* [Sleep Apnea](/sleepkit/tasks/apnea/)
diff --git a/docs/detection-annotated-dataset.md b/astro-site/src/content/docs/detection-annotated-dataset.mdx
similarity index 88%
rename from docs/detection-annotated-dataset.md
rename to astro-site/src/content/docs/detection-annotated-dataset.mdx
index c23fbb6..a3a5c60 100644
--- a/docs/detection-annotated-dataset.md
+++ b/astro-site/src/content/docs/detection-annotated-dataset.mdx
@@ -1,4 +1,7 @@
-# Training with the annotated-period target
+---
+title: "Training with the annotated-period target"
+description: "AnnotatedDataset composes the verified source, raw events, candidate policy, and frozen assignment as a read-only source adapter. It reconstructs the verified…"
+---
 
 `AnnotatedDataset` composes the verified source, raw events, candidate policy, and
 frozen assignment as a read-only source adapter. It reconstructs the verified UTC
@@ -6,7 +9,7 @@ clock and builds target arrays in memory. It neither loads historical
 `sleep_stages` values nor rewrites original files. This keeps target derivation
 versioned without duplicating the entire sensor dataset.
 
-```python
+```python title="Python example"
 from sleepkit.recipes.detection.target_dataset import AnnotatedDataset
 from sleepkit.recipes.detection.recipe import Config, run_membership
 
@@ -67,7 +70,7 @@ Every membership run retains these local files outside the public bundle:
 
 Index creation checks every eligible target against the evaluated target sequence,
 not only the total length. The index also rejects a different frozen context length.
-Its coordinates support the [common-index comparison](detection-comparison.md)
+Its coordinates support the [common-index comparison](/sleepkit/detection-comparison/)
 with historical model outputs. Test prediction collection
 currently retains batches in memory before writing NPZ; index writing streams by
 subject. This is an offline recipe, not a causal streaming detector.
@@ -89,8 +92,8 @@ historical labels, train-only fitted state, Keras/TFLite parity, exact scored ta
 metric reproduction from saved predictions, unlabeled inference, and artifact privacy.
 The legacy training/inference tests remain supported.
 
-The [first declared experiment](detection-first-experiment.md) is complete, with
+The [first declared experiment](/sleepkit/detection-first-experiment/) is complete, with
 pooled and per-series membership metrics and sampled real-input Keras/TFLite parity.
-Its read-only report can be reproduced from saved scoring evidence. The [common scoring intersection](detection-comparison.md) and
-[INT8 conversion](detection-int8.md) are documented separately. Historical training exposure, task acceptance thresholds, artifact
+Its read-only report can be reproduced from saved scoring evidence. The [common scoring intersection](/sleepkit/detection-comparison/) and
+[INT8 conversion](/sleepkit/detection-int8/) are documented separately. Historical training exposure, task acceptance thresholds, artifact
 licensing and hardware validation remain separate questions.
diff --git a/docs/detection-comparison.md b/astro-site/src/content/docs/detection-comparison.mdx
similarity index 94%
rename from docs/detection-comparison.md
rename to astro-site/src/content/docs/detection-comparison.mdx
index 6e718fc..f7fd451 100644
--- a/docs/detection-comparison.md
+++ b/astro-site/src/content/docs/detection-comparison.mdx
@@ -1,12 +1,15 @@
-# Detection diagnostics and historical comparison
+---
+title: "Detection diagnostics and historical comparison"
+description: "The first experiment now has read-only error diagnostics and a separate, explicit SD-2 comparison recipe. These steps consume the same saved run evidence. They…"
+---
 
-The [first experiment](detection-first-experiment.md) now has read-only error
+The [first experiment](/sleepkit/detection-first-experiment/) now has read-only error
 diagnostics and a separate, explicit SD-2 comparison recipe. These steps consume
 the same saved run evidence. They do not retrain a model or tune a threshold.
 
 ## Error diagnostics
 
-```sh
+```sh title="Terminal"
 python -m sleepkit.recipes.detection.error_analysis \
   --run /path/to/membership-run \
   --output /path/to/new-error-analysis.json
@@ -122,7 +125,7 @@ source, and output hashes also matched. No material finding remained.
 
 ## Compose the comparison in Python
 
-```python
+```python title="Python example"
 from sleepkit.recipes.detection.comparison import compare
 from sleepkit.recipes.detection.target_dataset import AnnotatedDataset
 
@@ -165,5 +168,5 @@ both flattened prediction arrays. These files contain identifiers and remain
 outside public bundles. Existing output directories are refused. Failed runs
 retain partial evidence without a completed comparison report.
 
-The next [fixed int8 conversion](detection-int8.md) preserves the new model's
+The next [fixed int8 conversion](/sleepkit/detection-int8/) preserves the new model's
 preprocessing and measures quantization separately from this historical comparison.
diff --git a/docs/detection-evaluation-protocol.md b/astro-site/src/content/docs/detection-evaluation-protocol.mdx
similarity index 91%
rename from docs/detection-evaluation-protocol.md
rename to astro-site/src/content/docs/detection-evaluation-protocol.mdx
index be66bf7..369cf35 100644
--- a/docs/detection-evaluation-protocol.md
+++ b/astro-site/src/content/docs/detection-evaluation-protocol.mdx
@@ -1,7 +1,9 @@
-# Detection evaluation checkpoint
-
-This page preserves a historical evaluation checkpoint. For the implemented workflow, see the [annotated dataset adapter](detection-annotated-dataset.md) and [comparison report](detection-comparison.md). The checkpoint discussion below records the decisions leading to that workflow.
+---
+title: "Detection evaluation checkpoint"
+description: "This page preserves a historical evaluation checkpoint. For the implemented workflow, see the annotated dataset adapter and comparison report. The checkpoint…"
+---
 
+This page preserves a historical evaluation checkpoint. For the implemented workflow, see the [annotated dataset adapter](/sleepkit/detection-annotated-dataset/) and [comparison report](/sleepkit/detection-comparison/). The checkpoint discussion below records the decisions leading to that workflow.
 
 The pipeline and artifact contracts are implemented. The next question is whether
 a comparison is valid, before asking which model scores better. This checkpoint
@@ -12,7 +14,7 @@ It does not change source labels, select a split, or establish benchmark results
 
 From the checkout, in an environment with NumPy and h5py:
 
-```sh
+```sh title="Terminal"
 python -m sleepkit.recipes.detection.audit \
   --data /path/to/cmidss \
   --events /path/to/cmidss/train_events.csv \
@@ -146,21 +148,20 @@ At this checkpoint, raw-series alignment and annotation-policy review were
 the next steps; their completed records are linked below. Historical model exposure and model licensing remain unresolved. No model
 publication is authorized by producing an audit report.
 
-The [event-supported label policy](detection-label-policy.md) records the
+The [event-supported label policy](/sleepkit/detection-label-policy/) records the
 subsequent review and adopted target. It does not overwrite the audit output.
 
-The [raw-source verification](detection-source-alignment.md) now confirms step/index,
+The [raw-source verification](/sleepkit/detection-source-alignment/) now confirms step/index,
 UTC cadence, and channel alignment for the audited local snapshot. Its 44 local-clock
 jumps coincide with timezone-offset changes. This supplies source-alignment evidence;
 annotation semantics and historical training exposure remain separate open questions.
 
-
 ## Current implementation status
 
 The audit, raw alignment, independent-clock path, candidate policy, and target-aware
-adapter are merged. The [frozen assignment and target contract](detection-frozen-split.md)
+adapter are merged. The [frozen assignment and target contract](/sleepkit/detection-frozen-split/)
 record the recovered primary annotation evidence and seed-zero evaluation cohort.
-The [first experiment](detection-first-experiment.md) and
-[diagnostics/common-index comparison](detection-comparison.md) now have local
+The [first experiment](/sleepkit/detection-first-experiment/) and
+[diagnostics/common-index comparison](/sleepkit/detection-comparison/) now have local
 results. Historical model exposure and artifact licensing remain unresolved;
 the comparison is descriptive rather than a verified historical held-out benchmark.
diff --git a/docs/detection-first-experiment.md b/astro-site/src/content/docs/detection-first-experiment.mdx
similarity index 93%
rename from docs/detection-first-experiment.md
rename to astro-site/src/content/docs/detection-first-experiment.mdx
index 3bb5190..58cec83 100644
--- a/docs/detection-first-experiment.md
+++ b/astro-site/src/content/docs/detection-first-experiment.mdx
@@ -1,4 +1,7 @@
-# First frozen-cohort membership experiment
+---
+title: "First frozen-cohort membership experiment"
+description: "The first new detection recipe run completed on 2026-09-20. Its final Keras model achieved 95.7657% accuracy and 0.955611 macro-F1 on the frozen test set. This…"
+---
 
 The first new detection recipe run completed on 2026-09-20. Its final Keras model
 achieved **95.7657% accuracy and 0.955611 macro-F1** on the frozen test set.
@@ -25,7 +28,7 @@ The frozen series assignment and retained contexts were:
 
 The split file SHA-256 is
 `08c2ac221b3212b5589d29f623015994da8c6fd104708879b40bcb4db60306e7`.
-The [annotated dataset adapter](detection-annotated-dataset.md) reconstructs targets
+The [annotated dataset adapter](/sleepkit/detection-annotated-dataset/) reconstructs targets
 from verified events and clocks; it ignores historical `sleep_stages` arrays.
 Preprocessing v3 uses five features, 60-second windows, and a 30-second stride.
 Normalization fits all valid frames from the 193 training series, including
@@ -96,7 +99,7 @@ For a completed membership run, the read-only evaluation helper needs NumPy and
 the local scoring evidence, but does not load TensorFlow, source recordings, or
 the model:
 
-```sh
+```sh title="Terminal"
 python -m sleepkit.recipes.detection.evaluation \
   --run /path/to/run \
   --output /path/to/new-evaluation.json
@@ -123,7 +126,7 @@ committed with this report.
 
 ## Next evidence gates
 
-The [error diagnostics and historical comparison](detection-comparison.md) now
+The [error diagnostics and historical comparison](/sleepkit/detection-comparison/) now
 report series, context-position, and observed-transition errors and compare both
 pipelines on an exact common scoring intersection. Any changes informed by these
 test reports must be declared; the current test set is no longer untouched for
diff --git a/docs/detection-frozen-split.md b/astro-site/src/content/docs/detection-frozen-split.mdx
similarity index 91%
rename from docs/detection-frozen-split.md
rename to astro-site/src/content/docs/detection-frozen-split.mdx
index ff6d180..5eaf998 100644
--- a/docs/detection-frozen-split.md
+++ b/astro-site/src/content/docs/detection-frozen-split.mdx
@@ -1,9 +1,12 @@
-# Frozen evaluation assignment and target
+---
+title: "Frozen evaluation assignment and target"
+description: "The reviewed audit/clock/candidate stack (#22–#25) is integrated into main. This checkpoint defines the derived target and freezes a reproducible split. It is…"
+---
 
 The reviewed audit/clock/candidate stack (#22–#25) is integrated into `main`.
 This checkpoint defines the derived target and freezes a reproducible split. It
 is assignment-only: running the existing recipe with `split.json` still reads
-historical HDF5 labels. The separate [annotated dataset recipe](detection-annotated-dataset.md) applies the
+historical HDF5 labels. The separate [annotated dataset recipe](/sleepkit/detection-annotated-dataset/) applies the
 derived target and writes exact scoring evidence.
 
 ## Target: annotated nightly-period membership
@@ -30,7 +33,7 @@ model output names remain unchanged; future dataset/model artifacts must carry
 
 Source retrieval (public, no credentials):
 
-```sh
+```sh title="Terminal"
 curl -s https://www.kaggle.com/api/i/competitions.PageService/ListPages \
   -H 'Content-Type: application/json' --data '{"competitionId":53666}'
 ```
@@ -43,7 +46,7 @@ identity statement; no independent person-linkage audit was performed.
 
 ## Deterministic assignment
 
-```sh
+```sh title="Terminal"
 uv run python -m sleepkit.recipes.detection.split \
   --data /path/to/original-cmidss --coverage /path/to/candidate-coverage.json \
   --output /path/to/new-frozen-split --seed 0
@@ -86,6 +89,6 @@ training exposure remains unknown, so this new split cannot certify that model
 as held out. Preserve historical preprocessing and treat its results as descriptive
 until exposure evidence exists.
 
-The [read-only target adapter](detection-annotated-dataset.md) now derives labels
+The [read-only target adapter](/sleepkit/detection-annotated-dataset/) now derives labels
 in memory from verified events, retains unknown masks, uses this frozen assignment,
 and persists exact scoring indices with target-specific artifact metadata.
diff --git a/docs/detection-golden.md b/astro-site/src/content/docs/detection-golden.mdx
similarity index 90%
rename from docs/detection-golden.md
rename to astro-site/src/content/docs/detection-golden.mdx
index 270d3c4..4d39b2d 100644
--- a/docs/detection-golden.md
+++ b/astro-site/src/content/docs/detection-golden.mdx
@@ -1,4 +1,7 @@
-# Detection golden baseline
+---
+title: "Detection golden baseline"
+description: "experiments/detection-golden.json defines a public reconstruction of the first annotated-period membership run. It records the exact recipe configuration,…"
+---
 
 `experiments/detection-golden.json` defines a public reconstruction of the first
 annotated-period membership run. It records the exact recipe configuration,
@@ -17,7 +20,7 @@ This is a reconstruction definition, not a universal runner or a release
 manifest. The ordinary Python entry point validates the supplied local evidence
 against the pinned aggregate hashes and then calls `run_membership`:
 
-```sh
+```sh title="Terminal"
 python experiments/run_detection_golden.py \
   --data /path/to/original-cmidss \
   --events /path/to/train_events.csv \
@@ -40,7 +43,7 @@ definition and runner hash remain attached to that run.
 
 Performance measurements are a separate concern; use the bounded ordinary-Python
 profiler at `python -m sleepkit.recipes.detection.profile` with the same local
-dataset evidence; see the [profiling guide](detection-profiling.md). Its results
+dataset evidence; see the [profiling guide](/sleepkit/detection-profiling/). Its results
 are not part of this golden quality reference.
 
 The target is `sleepkit.annotated_period_membership/v1`: class 0 is
@@ -84,8 +87,8 @@ These are observed reference values from one recorded run. They are not
 prospective acceptance thresholds or quality gates. Seeded execution is not a
 bitwise reproducibility guarantee across environments, and a run from the current
 checkout is expected to be a reconstruction rather than a byte-identical replay.
-The [first experiment report](detection-first-experiment.md) and
-[annotated target adapter](detection-annotated-dataset.md) provide the surrounding
+The [first experiment report](/sleepkit/detection-first-experiment/) and
+[annotated target adapter](/sleepkit/detection-annotated-dataset/) provide the surrounding
 evidence and interpretation. Exposure of a separate historical SD-2 comparator to
 this cohort remains unresolved; that question does not qualify this newly trained
 frozen-split baseline.
@@ -94,6 +97,6 @@ The vectorized preparation revision advances only the current preprocessing
 implementation pin. The original module hashes remain under `historical_run`, and
 the original definition remains available at commit
 `042c1eef5608db3bb842f29c6f12b768663f9026`. The
-[preparation equivalence report](detection-preparation-performance.md) binds the
+preparation equivalence report binds the
 revised implementation to exact scalar-reference comparisons. Historical quality
 metrics remain observations from the original five-epoch run.
diff --git a/docs/detection-int8.md b/astro-site/src/content/docs/detection-int8.mdx
similarity index 93%
rename from docs/detection-int8.md
rename to astro-site/src/content/docs/detection-int8.mdx
index 07ba35a..86fe6d6 100644
--- a/docs/detection-int8.md
+++ b/astro-site/src/content/docs/detection-int8.mdx
@@ -1,13 +1,16 @@
-# Fixed int8 conversion and release preparation
+---
+title: "Fixed int8 conversion and release preparation"
+description: "This step converts the first membership experiment without retraining or replacing its fitted preprocessing. It preserves the original float TFLite and Keras…"
+---
 
-This step converts the [first membership experiment](detection-first-experiment.md)
+This step converts the [first membership experiment](/sleepkit/detection-first-experiment/)
 without retraining or replacing its fitted preprocessing. It preserves the original
 float TFLite and Keras bytes and measures the quantized model on exactly the same
 eligible test outputs.
 
 ## Run the Python recipe
 
-```python
+```python title="Python example"
 from sleepkit.recipes.detection.quantization import quantize_run
 
 # source is the same AnnotatedDataset used for the original run.
@@ -116,7 +119,7 @@ per model with bitwise-identical outputs. No material finding remained.
 
 The existing raw-sensor entry point supports both bundled models:
 
-```python
+```python title="Python example"
 from sleepkit.recipes.detection.inference import predict
 
 integer = predict("int8-run/bundle", sensor_data, sample_time=utc_seconds)
@@ -142,11 +145,11 @@ The local experiment directory additionally retains the declaration, private
 calibration evidence, per-series evaluation, and exact int8/float test arrays.
 Staging refuses existing output directories and preserves the original run.
 Once release rights and terms are established, use the generic
-[`stage-release` operation](huggingface-artifacts.md#stage-a-licensed-release-from-an-experiment)
+[`stage-release` operation](/sleepkit/huggingface-artifacts/#stage-a-licensed-release-from-an-experiment)
 to add the selected license, public decision record and release card without
 reconverting the models or replacing their validation evidence.
 
-```sh
+```sh title="Terminal"
 python -m sleepkit.artifacts validate /path/to/int8-run/bundle \
   --profile runnable --runtime
 python -m sleepkit.artifacts publish /path/to/int8-run/bundle \
@@ -156,7 +159,7 @@ python -m sleepkit.artifacts publish /path/to/int8-run/bundle \
 The second command is a local dry run. The repository name is a proposed dedicated
 destination, not an existing deployment. Upload requires an explicitly chosen model
 artifact license and the `--upload` option; source-code licensing is not assumed to
-cover model weights. The [licensing policy](model-licensing-policy.md) records
+cover model weights. The [licensing policy](/sleepkit/model-licensing-policy/) records
 CC BY-NC-SA 4.0 without an Ambiq hardware restriction as the conservative candidate
 for this CMIDSS model. Data-access, training-purpose and model-redistribution facts
 still need to be recorded before release; choosing an NC label alone does not
diff --git a/docs/detection-label-policy.md b/astro-site/src/content/docs/detection-label-policy.mdx
similarity index 90%
rename from docs/detection-label-policy.md
rename to astro-site/src/content/docs/detection-label-policy.mdx
index be9fdb0..7ecb177 100644
--- a/docs/detection-label-policy.md
+++ b/astro-site/src/content/docs/detection-label-policy.mdx
@@ -1,7 +1,9 @@
-# Candidate event-supported label policy
-
-This page preserves a historical evaluation checkpoint. For the implemented workflow, see the [annotated dataset adapter](detection-annotated-dataset.md) and [comparison report](detection-comparison.md). The checkpoint discussion below records the decisions leading to that workflow.
+---
+title: "Candidate event-supported label policy"
+description: "This page preserves a historical evaluation checkpoint. For the implemented workflow, see the annotated dataset adapter and comparison report. The checkpoint…"
+---
 
+This page preserves a historical evaluation checkpoint. For the implemented workflow, see the [annotated dataset adapter](/sleepkit/detection-annotated-dataset/) and [comparison report](/sleepkit/detection-comparison/). The checkpoint discussion below records the decisions leading to that workflow.
 
 Status: implemented candidate policy `sleepkit.event_candidates/v1`, separate from
 the active training label policy. No existing HDF5 labels or historical model inputs are rewritten here.
@@ -66,7 +68,7 @@ available event timestamp directly with raw UTC at its source step (pair duratio
 alone cannot detect an equal shift of both timestamps), and test a pure interval
 builder that enforces consecutive integer night IDs and excludes incomplete or
 conflicting intervening groups. The optional event-clock audit now implements the first check; see
-[independent sample clocks](detection-sample-clock.md). The pure interval builder and read-only coverage audit are now implemented.
+[independent sample clocks](/sleepkit/detection-sample-clock/). The pure interval builder and read-only coverage audit are now implemented.
 Annotation-semantics review remains pending before these candidates become
 evaluation ground truth.
 
@@ -77,13 +79,12 @@ sample clock. A source alignment report can establish that a particular snapshot
 is continuous in UTC even when local clocks jump. That finding does not justify
 accepting arbitrary one-hour jumps from other inputs.
 
-The [verified source adapter](detection-sample-clock.md) supplies an explicit UTC sample
+The [verified source adapter](/sleepkit/detection-sample-clock/) supplies an explicit UTC sample
 clock alongside local TS. Validate cadence against that clock, use local TS only
 for time-of-day features, and carry the clock contract through training and
 unlabeled inference. Keep the existing strict check for inputs without sufficient
 clock evidence. Do not add a global option that simply disables cadence checks.
 
-
 ## Pure builder and conflict rules
 
 `candidate_labels.build_candidates(nights, sample_count, first_utc_seconds=...)`
@@ -108,7 +109,7 @@ nights. Shuffling rows and groups leaves the result unchanged.
 
 ## Read-only coverage audit
 
-```sh
+```sh title="Terminal"
 uv run python -m sleepkit.recipes.detection.candidate_audit \
   --data /path/to/original-cmidss --events /path/to/train_events.csv \
   --alignment /path/to/passed-event-clock-alignment.json \
@@ -149,10 +150,10 @@ population, but this is not a model benchmark or evidence of label correctness.
 The [official competition overview](https://www.kaggle.com/competitions/child-mind-institute-detect-sleep-states)
 defines an event-detection task evaluated by average precision across timestamp
 tolerances. This does not by itself establish dense clinical sleep/wake ground truth.
-The [retrieved annotation contract and frozen split](detection-frozen-split.md)
+The [retrieved annotation contract and frozen split](/sleepkit/detection-frozen-split/)
 now support a derived annotated-period membership target, with explicit inside,
 outside-supported-period, and unknown semantics. They do not support clinical
 per-sample sleep/wake claims. The builder's historical candidate names remain
 unchanged; future training artifacts must declare the adopted target explicitly.
-The [annotated dataset adapter and scoring index](detection-annotated-dataset.md)
+The [annotated dataset adapter and scoring index](/sleepkit/detection-annotated-dataset/)
 now implement this contract without changing original labels.
diff --git a/docs/detection-profiling.md b/astro-site/src/content/docs/detection-profiling.mdx
similarity index 92%
rename from docs/detection-profiling.md
rename to astro-site/src/content/docs/detection-profiling.mdx
index 184b5ca..658126e 100644
--- a/docs/detection-profiling.md
+++ b/astro-site/src/content/docs/detection-profiling.mdx
@@ -1,4 +1,7 @@
-# Profiling the detection recipe
+---
+title: "Profiling the detection recipe"
+description: "The profiler measures the existing sleepKIT TensorFlow membership recipe before we change its loader. It calls the same feature preparation, normalization,…"
+---
 
 The profiler measures the existing sleepKIT TensorFlow membership recipe before
 we change its loader. It calls the same feature preparation, normalization,
@@ -8,9 +11,9 @@ validation/test predictions, choose a checkpoint, export a model or publish to H
 Whole-cohort integrity checks still hash the frozen source files.
 
 Run from a fresh process with the detection dependencies installed. Use the
-frozen dataset inputs described in [the golden experiment](detection-golden.md):
+frozen dataset inputs described in [the golden experiment](/sleepkit/detection-golden/):
 
-```sh
+```sh title="Terminal"
 TF_NUM_INTRAOP_THREADS=2 TF_NUM_INTEROP_THREADS=1 OMP_NUM_THREADS=2 \
 python -m sleepkit.recipes.detection.profile \
   --data "$DATA" --events "$EVENTS" \
@@ -85,7 +88,7 @@ Keep prediction-quality reproduction separate from performance experiments.
 
 ## First CPU observation
 
-The [2026-09-20 evidence](evidence/membership-tcn-seed0-cpu-20260920/report.json)
+The [2026-09-20 evidence](/sleepkit/evidence/membership-tcn-seed0-cpu-20260920/report.json)
 contains the declaration, environment and report for the frozen membership training
 partition: 193 series, 14,857,307 valid normalization frames, 34,474 eligible
 contexts, 1,078 batches, and ten contexts in the final batch. No held-out feature
@@ -116,4 +119,4 @@ do not establish a TensorFlow backend bottleneck. Candidate optimizations must
 preserve the recorded feature/normalizer and sample contracts and be compared in
 repeated, controlled runs before any improvement claim.
 
-The next measured change is [bounded vectorized feature preparation](detection-preparation-performance.md), with exact scalar-reference comparisons before reusing the existing feature contract.
+The next measured change is bounded vectorized feature preparation, with exact scalar-reference comparisons before reusing the existing feature contract.
diff --git a/docs/detection-recipe.md b/astro-site/src/content/docs/detection-recipe.mdx
similarity index 90%
rename from docs/detection-recipe.md
rename to astro-site/src/content/docs/detection-recipe.mdx
index d897de6..e13f669 100644
--- a/docs/detection-recipe.md
+++ b/astro-site/src/content/docs/detection-recipe.mdx
@@ -1,10 +1,13 @@
-# A detection pipeline written in Python
+---
+title: "A detection pipeline written in Python"
+description: "The new detection recipe is developed alongside the legacy task CLI. It reads CMIDSS sensor-channel HDF5 files and owns feature generation, normalization,…"
+---
 
 The new detection recipe is developed alongside the legacy task CLI. It reads
 CMIDSS sensor-channel HDF5 files and owns feature generation, normalization,
 training, held-out evaluation, and artifact export. It uses ordinary functions,
 a small config, a native Keras model, and the shared artifact package from
-[the HF publishing workflow](huggingface-artifacts.md).
+[the HF publishing workflow](/sleepkit/huggingface-artifacts/).
 
 This is an experimental major-version building block. It does not reproduce the
 historical SD-2-TCN-SM model. Global training-only normalization, invalid-data
@@ -19,7 +22,7 @@ CPU. TensorBoard is now an explicit dependency because legacy training uses its
 callback and TF 2.21 no longer brings it in transitively. The original SleepKit
 training dependencies remain installed; a full dependency split is later work.
 
-```sh
+```sh title="Terminal"
 uv sync --extra detection
 uv run --extra detection python -m sleepkit.recipes.detection smoke --output /tmp/detection-smoke
 ```
@@ -63,7 +66,7 @@ pair sleep events, or silently resample irregular signals.
 
 Write a local subject split with three nonempty, disjoint partitions:
 
-```json
+```json title="JSON example"
 {
   "train": ["subject-a", "subject-b"],
   "validation": ["subject-c"],
@@ -76,7 +79,7 @@ contributes to fitted normalization. For datasets with multiple recordings per
 person, the adapter must group them under the same subject before creating splits;
 this CMIDSS adapter treats each source series file as one subject.
 
-```sh
+```sh title="Terminal"
 uv run --extra detection python -m sleepkit.recipes.detection train \
   --data /path/to/cmidss --split /path/to/split.json \
   --output /path/to/new-run --cache /path/to/local-feature-cache \
@@ -98,7 +101,7 @@ of each source timestamp: `mean(cos(2*pi*TS/86400))`. Encoding before averaging
 keeps windows spanning midnight close to +1 rather than interpreting them as noon.
 The v2 contract corrected the original time-of-day formula. The default v3
 contract additionally validates continuity with an independent UTC sample clock;
-see [sample-clock alignment](detection-sample-clock.md).
+see [sample-clock alignment](/sleepkit/detection-sample-clock/).
 Old v1 caches are bypassed, and v1 fitted states are rejected by this implementation.
 Retrain and export a new bundle; do not pair v1 model weights or normalization with
 v2 or v3 features. Historical baseline packaging and its original feature requirements
@@ -129,7 +132,7 @@ an incremental reader in a later adapter.
 
 ## Compose an experiment
 
-```python
+```python title="Python example"
 from sleepkit.recipes.detection.recipe import Config, run
 
 run(data_root, split_file, output_dir, Config(epochs=10),
@@ -145,7 +148,7 @@ This logits contract belongs to this recipe, not the generic artifact format.
 
 Experiments can also import and call individual steps directly:
 
-```python
+```python title="Python example"
 from sleepkit.recipes.detection.data import load_split, subject_features
 from sleepkit.recipes.detection.preprocessing import Normalizer
 
@@ -182,7 +185,7 @@ fingerprints in the bundle verify a known local run, but cannot reconstruct its
 private data. Metrics report the provided labels, not clinical accuracy. No task
 acceptance threshold is implied by successful evaluation or runtime conformance.
 
-```sh
+```sh title="Terminal"
 python -m sleepkit.artifacts validate /path/to/run/bundle --profile runnable --runtime
 python -m sleepkit.recipes.detection predict \
   --bundle /path/to/run/bundle --data /path/to/cmidss \
@@ -198,6 +201,6 @@ using the artifact API. License choice and publication are separate release work
 
 ## Validation scope
 
-The [first experiment](detection-first-experiment.md), [historical comparison](detection-comparison.md), and [INT8 conversion](detection-int8.md) document separate completed checkpoints and their evidence. Read their declared populations, target semantics and limitations before comparing results. These reports do not establish MCU validation or equivalence to a clinical sleep/wake task.
+The [first experiment](/sleepkit/detection-first-experiment/), [historical comparison](/sleepkit/detection-comparison/), and [INT8 conversion](/sleepkit/detection-int8/) document separate completed checkpoints and their evidence. Read their declared populations, target semantics and limitations before comparing results. These reports do not establish MCU validation or equivalence to a clinical sleep/wake task.
 
-The export path uses the native [Keras export API](https://keras.io/api/models/model_saving_apis/export/) and [TensorFlow-to-LiteRT conversion](https://developers.google.com/edge/litert/conversion/tensorflow/convert_tf). The [evaluation checkpoint](detection-evaluation-protocol.md) records the annotation audit and comparison requirements.
+The export path uses the native [Keras export API](https://keras.io/api/models/model_saving_apis/export/) and [TensorFlow-to-LiteRT conversion](https://developers.google.com/edge/litert/conversion/tensorflow/convert_tf). The [evaluation checkpoint](/sleepkit/detection-evaluation-protocol/) records the annotation audit and comparison requirements.
diff --git a/docs/detection-sample-clock.md b/astro-site/src/content/docs/detection-sample-clock.mdx
similarity index 92%
rename from docs/detection-sample-clock.md
rename to astro-site/src/content/docs/detection-sample-clock.mdx
index 6ecd7f5..8c45a6d 100644
--- a/docs/detection-sample-clock.md
+++ b/astro-site/src/content/docs/detection-sample-clock.mdx
@@ -1,4 +1,7 @@
-# Independent sample clocks
+---
+title: "Independent sample clocks"
+description: "Detection preprocessing v3 accepts local TS/ENMO/ZANGLE values plus an optional independent sampletime vector. It must contain signed int64 Unix seconds, align…"
+---
 
 Detection preprocessing v3 accepts local TS/ENMO/ZANGLE values plus an optional
 independent `sample_time` vector. It must contain signed int64 Unix seconds, align
@@ -6,7 +9,7 @@ one-to-one with source samples, and increase by exactly five seconds. UTC contro
 sampling continuity; local TS still supplies the cyclic time-of-day feature. Without
 an independent clock, the existing strict local-TS cadence check applies.
 
-```python
+```python title="Python example"
 from sleepkit.recipes.detection.preprocessing import prepare
 from sleepkit.recipes.detection.inference import predict
 
@@ -37,11 +40,11 @@ Use a fresh source-alignment report containing `first_utc_seconds` to build a se
 clocked dataset. Only a successful report with matching source hashes can support
 reconstruction of the regular UTC sequence. Older reports must be regenerated.
 The clock materializer preserves historical labels; it does not implement the
-[candidate label policy](detection-label-policy.md).
+[candidate label policy](/sleepkit/detection-label-policy/).
 
 ## Event clock evidence
 
-```sh
+```sh title="Terminal"
 uv run --extra source-audit python -m sleepkit.recipes.detection.alignment \
   --data /path/to/cmidss --parquet /path/to/train_series.parquet \
   --events /path/to/train_events.csv --output /path/to/new-alignment.json
@@ -55,7 +58,7 @@ requested audit; an entirely missing event set does not pass. The report binds t
 events file hash and detects file changes during verification.
 
 This verifies event timing, not sleep/wake semantics, independent-person grouping,
-or historical model training exposure. The [candidate interval builder and coverage audit](detection-label-policy.md) are
+or historical model training exposure. The [candidate interval builder and coverage audit](/sleepkit/detection-label-policy/) are
 implemented separately. Annotation-protocol review remains a gate before benchmarking.
 
 On the audited local snapshot, all 9,585 available event rows match raw UTC exactly;
@@ -66,7 +69,7 @@ These results apply only to the source hashes in the local report.
 
 To copy explicitly selected subjects with verified clocks:
 
-```sh
+```sh title="Terminal"
 uv run python -m sleepkit.recipes.detection.clock_source \
   --data /path/to/cmidss --alignment /path/to/new-alignment.json \
   --output /path/to/new-clocked-source --subjects subject_a subject_b
diff --git a/docs/detection-source-alignment.md b/astro-site/src/content/docs/detection-source-alignment.mdx
similarity index 87%
rename from docs/detection-source-alignment.md
rename to astro-site/src/content/docs/detection-source-alignment.mdx
index a5336aa..cabac69 100644
--- a/docs/detection-source-alignment.md
+++ b/astro-site/src/content/docs/detection-source-alignment.mdx
@@ -1,10 +1,13 @@
-# Raw-source alignment verification
+---
+title: "Raw-source alignment verification"
+description: "This check establishes whether the legacy HDF5 sensor rows match the raw Parquet series, independently of local time-of-day changes. It is separate from…"
+---
 
 This check establishes whether the legacy HDF5 sensor rows match the raw Parquet
 series, independently of local time-of-day changes. It is separate from annotation
 coverage, person identity, and model evaluation.
 
-```sh
+```sh title="Terminal"
 uv sync --extra detection --extra source-audit
 uv run --extra source-audit python -m sleepkit.recipes.detection.alignment \
   --data /path/to/cmidss --parquet /path/to/cmidss/train_series.parquet \
@@ -48,13 +51,13 @@ person, or that historical model training excluded any particular subject.
 ## Reader change supported by this evidence
 
 The legacy detector path validates cadence using local TS and rejects these 44 files.
-The [independent sample-clock adapter](detection-sample-clock.md) now preserves an explicit UTC clock alongside
+The [independent sample-clock adapter](/sleepkit/detection-sample-clock/) now preserves an explicit UTC clock alongside
 local TS, validates continuity against it, and uses local TS only for the time-of-day
 feature. This contract carries through caching, training, and unlabeled inference.
 Inputs without independent clock evidence retain the strict local-TS check.
 
-The [event-supported label policy](detection-label-policy.md) records the adopted
-derived annotated-period membership target. The [dataset adapter](detection-annotated-dataset.md)
+The [event-supported label policy](/sleepkit/detection-label-policy/) records the adopted
+derived annotated-period membership target. The [dataset adapter](/sleepkit/detection-annotated-dataset/)
 implements that target without overwriting source labels.
 
 ## Parquet dependency compatibility
diff --git a/docs/features/byofs.md b/astro-site/src/content/docs/features/byofs.mdx
similarity index 82%
rename from docs/features/byofs.md
rename to astro-site/src/content/docs/features/byofs.mdx
index cae028a..b638491 100644
--- a/docs/features/byofs.md
+++ b/astro-site/src/content/docs/features/byofs.mdx
@@ -1,4 +1,7 @@
-# Bring-Your-Own-Features (BYOFS)
+---
+title: "Bring-Your-Own-Features (BYOFS)"
+description: "The Bring-Your-Own-Features (BYOFS) allows users to add custom feature sets to sleepKIT to be used with built-in or custom tasks."
+---
 
 The Bring-Your-Own-Features (BYOFS) allows users to add custom feature sets to sleepKIT to be used with built-in or custom tasks.
 
@@ -6,7 +9,7 @@ The Bring-Your-Own-Features (BYOFS) allows users to add custom feature sets to s
 
 1. **Create a Feature Set**: Define a new feature set class that subclasses `sk.FeatureSet` and implements the feature contract. The following is a skeleton: implement the writer before calling it.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
 
     class CustomFeatureSet(sk.FeatureSet):
@@ -25,14 +28,14 @@ The Bring-Your-Own-Features (BYOFS) allows users to add custom feature sets to s
 
 2. **Register the Feature Set**: Register the new feature set with the `sk.FeatureFactory` by calling the `register` method. This method takes the feature set name and the feature set class as arguments.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
     sk.FeatureFactory.register(CustomFeatureSet.name(), CustomFeatureSet)
     ```
 
 3. **Use the Feature Set**: The new feature set can now be used to generate feature sets.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
 
     from pathlib import Path
diff --git a/docs/features/fs_c_ear_9.md b/astro-site/src/content/docs/features/fs_c_ear_9.mdx
similarity index 77%
rename from docs/features/fs_c_ear_9.md
rename to astro-site/src/content/docs/features/fs_c_ear_9.mdx
index 4e53777..e109f1e 100644
--- a/docs/features/fs_c_ear_9.md
+++ b/astro-site/src/content/docs/features/fs_c_ear_9.mdx
@@ -1,4 +1,7 @@
-# Feature Set: FS-C-EAR-9
+---
+title: "Feature Set: FS-C-EAR-9"
+description: "This feature set is targeted for sleep stage classification based on sensor data available from chest location. The feature set computes heart rate (HR), heart…"
+---
 
 ## Overview
 
@@ -14,7 +17,7 @@ The target location for this feature set is the __chest__. From this location, t
 
 ## Dataset Support
 
-- **[MESA](../datasets/mesa.md)**: This dataset does not directly provide accelerometer data from the chest. However, the dataset does provide respiratory signals (RIP) captured from both chest and abdomen. In place of accelerometer data, we use filtered chest respiratory signals as a proxy for body movement features.
+- **[MESA](/sleepkit/datasets/mesa/)**: This dataset does not directly provide accelerometer data from the chest. However, the dataset does provide respiratory signals (RIP) captured from both chest and abdomen. In place of accelerometer data, we use filtered chest respiratory signals as a proxy for body movement features.
 
 ## Features
 
@@ -32,7 +35,6 @@ This feature set includes the following 9 features:
 | rsp_bpm | Mean respiration rate derived from the respiratory signal | RSP |
 | hrv_qos | Quality of signal derived from HRV | ECG |
 
-
 ## Output
 
 The feature set is stored as HDF5 files (`.h5`) with one file per subject with path: `{save_path}/{dataset}/{subject_id}.h5`. Each HDF5 file includes the following entries:
diff --git a/docs/features/fs_h_e_10.md b/astro-site/src/content/docs/features/fs_h_e_10.mdx
similarity index 83%
rename from docs/features/fs_h_e_10.md
rename to astro-site/src/content/docs/features/fs_h_e_10.mdx
index 455193c..ff85971 100644
--- a/docs/features/fs_h_e_10.md
+++ b/astro-site/src/content/docs/features/fs_h_e_10.mdx
@@ -1,4 +1,7 @@
-# Feature Set: FS-H-E-10
+---
+title: "Feature Set: FS-H-E-10"
+description: "This feature set is targeted for sleep stage classification based on single pair of EEG and EOG sensor data collected on head location. The feature set…"
+---
 
 ## Overview
 
@@ -13,7 +16,7 @@ The target location for this feature set is the __head__. From this location, th
 
 ## Dataset Support
 
-- **[MESA](../datasets/mesa.md)**: This dataset provides EEG, EOG, and EMG data from the head location. The dataset also provides sleep stage labels.
+- **[MESA](/sleepkit/datasets/mesa/)**: This dataset provides EEG, EOG, and EMG data from the head location. The dataset also provides sleep stage labels.
 
 ## Features
 
diff --git a/docs/features/fs_w_a_5.md b/astro-site/src/content/docs/features/fs_w_a_5.mdx
similarity index 78%
rename from docs/features/fs_w_a_5.md
rename to astro-site/src/content/docs/features/fs_w_a_5.mdx
index 9d6a48e..7d5ebda 100644
--- a/docs/features/fs_w_a_5.md
+++ b/astro-site/src/content/docs/features/fs_w_a_5.mdx
@@ -1,4 +1,7 @@
-# Feature Set: FS-W-A-5
+---
+title: "Feature Set: FS-W-A-5"
+description: "This feature set is targeted towards actigraphy style sleep detection using only IMU based sensor data available from wrist location. The feature set computes…"
+---
 
 ## Overview
 
@@ -12,7 +15,7 @@ The target location for this feature set is the __wrist__. From this location, t
 
 ## Dataset Support
 
-- **[CMIDSS](../datasets/cmidss.md)**: This dataset provides pre-computed movement and z-angle data from the wrist location every 5 seconds.
+- **[CMIDSS](/sleepkit/datasets/cmidss/)**: This dataset provides pre-computed movement and z-angle data from the wrist location every 5 seconds.
 
 ## Features
 
@@ -26,7 +29,6 @@ This feature set includes the following 5 features:
 | angle_mu | Mean z-angle | IMU |
 | angle_std | Standard deviation of z-angle | IMU |
 
-
 ## Output
 
 The feature set is stored as HDF5 files (`.h5`) with one file per subject with path: `{save_path}/{dataset}/{subject_id}.h5`. Each HDF5 file includes the following entries:
diff --git a/docs/features/fs_w_p_5.md b/astro-site/src/content/docs/features/fs_w_p_5.mdx
similarity index 78%
rename from docs/features/fs_w_p_5.md
rename to astro-site/src/content/docs/features/fs_w_p_5.mdx
index 898a203..eed6aaf 100644
--- a/docs/features/fs_w_p_5.md
+++ b/astro-site/src/content/docs/features/fs_w_p_5.mdx
@@ -1,4 +1,7 @@
-# Feature Set: FS-W-P-5
+---
+title: "Feature Set: FS-W-P-5"
+description: "This feature set is targeted for sleep apnea classification based on sensor data available from wrist location. The generator computes various features over…"
+---
 
 ## Overview
 
@@ -12,7 +15,7 @@ The target location for this feature set is the __wrist__. From this location, t
 
 ## Dataset Support
 
-- **[MESA](../datasets/mesa.md)**: This dataset does not directly provide dual PPG, however, the dataset does provide SpO2 and single channel PPG which is sufficient.
+- **[MESA](/sleepkit/datasets/mesa/)**: This dataset does not directly provide dual PPG, however, the dataset does provide SpO2 and single channel PPG which is sufficient.
 
 ## Features
 
diff --git a/docs/features/fs_w_pa_14.md b/astro-site/src/content/docs/features/fs_w_pa_14.mdx
similarity index 79%
rename from docs/features/fs_w_pa_14.md
rename to astro-site/src/content/docs/features/fs_w_pa_14.mdx
index af9a3e8..c5af344 100644
--- a/docs/features/fs_w_pa_14.md
+++ b/astro-site/src/content/docs/features/fs_w_pa_14.mdx
@@ -1,4 +1,7 @@
-# Feature Set: FS-W-PA-14
+---
+title: "Feature Set: FS-W-PA-14"
+description: "This feature set is targeted for sleep stage classification based on sensor data available from wrist location. The feature set computes heart rate, heart rate…"
+---
 
 ## Overview
 
@@ -13,7 +16,7 @@ The target location for this feature set is the __wrist__. From this location, t
 
 ## Dataset Support
 
-- **[MESA](../datasets/mesa.md)**: This dataset does not directly provide dual PPG nor Accelerometer data from the wrist. However, the dataset does provide SpO2 and single channel PPG which is sufficient for PPG derived features. In place of Accelerometer data, we use leg movement as a proxy for arm movement features.
+- **[MESA](/sleepkit/datasets/mesa/)**: This dataset does not directly provide dual PPG nor Accelerometer data from the wrist. However, the dataset does provide SpO2 and single channel PPG which is sufficient for PPG derived features. In place of Accelerometer data, we use leg movement as a proxy for arm movement features.
 
 ## Features
 
@@ -36,7 +39,6 @@ This feature set includes the following 14 features:
 | spo2_qos | Quality of signal derived from SpO2 | PPG |
 | hrv_qos | Quality of signal derived from HRV | PPG |
 
-
 ## Output
 
 The feature set is stored as HDF5 files (`.h5`) with one file per subject with path: `{save_path}/{dataset}/{subject_id}.h5`. Each HDF5 file includes the following entries:
diff --git a/astro-site/src/content/docs/features/index.mdx b/astro-site/src/content/docs/features/index.mdx
new file mode 100644
index 0000000..bc6b658
--- /dev/null
+++ b/astro-site/src/content/docs/features/index.mdx
@@ -0,0 +1,173 @@
+---
+title: "Features"
+description: "sleepKIT includes a mini feature store to enable crafting rich feature sets to train and evaluate models. The store includes several built-in feature set…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+__sleepKIT__ includes a mini _feature store_ to enable crafting rich feature sets to train and evaluate models. The store includes several built-in feature set generators that can be invoked to create feature sets for a variety of use cases. Each feature set inherits from `sk.FeatureSet` and provides implementations for generating features from the dataset.
+
+The main method, `generate_subject_features` receives the target dataset name, subject id, and high-level `sk.TaskParams` which includes field `feature` which includes feature set specific parameters. The resulting data and labels are stored in HDF5 format that can then be used for training via `sk.H5Dataloader` class. Custom feature set generators can also be added to the feature store by subclassing `sk.FeatureSet` and registering the new generator with the feature store.
+
+<div class="annotate">
+
+1. Load the configuration data (e.g. `configuration.json`)
+1. Load datasets from DatasetFactory (e.g. `mesa`)
+1. Load feature generator from FeatureFactory (e.g. `FS-W-PA-14`)
+1. Generate features for each dataset/subject (e.g. `generate_subject_features`)
+1. Save subjects features to disk (e.g. `subject.h5`)
+
+</div>
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="features-index-1.json" download="/sleepkit/examples/features-index-1.json">
+
+```json title="features-index-1.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+<br/>
+
+```mermaid
+graph TD
+A("`Load
+configuration
+__TaskParams__
+`")
+B("`Load datasets
+__DatasetFactory__
+`")
+C("`Load feature
+generator
+__FeatureFactory__
+`")
+D("`Generate
+features
+`")
+E("`Save
+features
+`")
+
+A ==> B
+B ==> C
+C ==> D
+D ==> E
+
+```
+
+---
+
+## Available Feature Sets
+
+- **[FS-W-PA-14](/sleepkit/features/fs_w_pa_14/)**:__14__ features derived from __PPG__ and __IMU__ on __wrist__ for __sleep stage__ classification.
+- **[FS-C-EAR-9](/sleepkit/features/fs_c_ear_9/)**: __9__ features derived from __ECG__, __RSP__, and __IMU__ on __chest__ for __sleep stage__ classification.
+- **[FS-W-A-5](/sleepkit/features/fs_w_a_5/)**: __5__ features derived from __IMU__ on __wrist__ for actigraphy style __sleep detection__.
+- **[FS-H-E-10](/sleepkit/features/fs_h_e_10/)**: __10__ features derived from __ECG__ and __EOG__ on __head__ for __sleep stage__ classification.
+- **[FS-W-P-5](/sleepkit/features/fs_w_p_5/)**: __5__ features derived from __PPG__ on __wrist__ for __sleep apnea__ classification.
+- **[BYOFS](/sleepkit/features/byofs/)**: Bring-Your-Own-Feature-Set (BYOFS) to create custom feature sets.
+
+## Feature Factory
+
+The feature factory, `sk.FeatureFactory`, provides a convenient way to access the feature sets. The factory is a thread-safe singleton class that provides a single point of access to the features via the features' slug names. The benefit of using the factory is it allows registering new additional feature sets that can then be leveraged by existing and new tasks.
+
+The feature factory provides the following methods:
+
+* **sk.FeatureFactory.register**: Register a custom feature set
+* **sk.FeatureFactory.unregister**: Unregister a custom feature set
+* **sk.FeatureFactory.has**: Check if a feature set is registered
+* **sk.FeatureFactory.get**: Get a feature set
+* **sk.FeatureFactory.list**: List all available feature sets
diff --git a/astro-site/src/content/docs/guides/index.mdx b/astro-site/src/content/docs/guides/index.mdx
new file mode 100644
index 0000000..76072a0
--- /dev/null
+++ b/astro-site/src/content/docs/guides/index.mdx
@@ -0,0 +1,34 @@
+---
+title: "User guide"
+description: "Build a sleepKIT workflow from datasets, feature extraction and model architectures. Use the guides below to configure each step, then follow a tutorial for a…"
+---
+
+Build a sleepKIT workflow from datasets, feature extraction and model architectures. Use the guides below to configure each step, then follow a tutorial for a complete experiment.
+
+## Run an experiment
+
+Start with a task configuration and work through the modes you need.
+
+<a className="md-button" href="/sleepkit/modes/configuration/">Configure a run</a>
+<a className="md-button" href="/sleepkit/modes/download/">Download data</a>
+<a className="md-button" href="/sleepkit/modes/train/">Train</a>
+<a className="md-button" href="/sleepkit/modes/evaluate/">Evaluate</a>
+<a className="md-button" href="/sleepkit/modes/export/">Export</a>
+<a className="md-button" href="/sleepkit/modes/demo/">Run a demo</a>
+
+## Choose your components
+
+- **Datasets** provide recordings and labels for your task. Review each dataset's access requirements and terms.
+- **Feature sets** transform recordings into inputs for training and evaluation.
+- **Model architectures** define the network and its configurable parameters.
+
+<a className="md-button" href="/sleepkit/datasets/">Browse datasets</a>
+<a className="md-button" href="/sleepkit/features/">Explore feature sets</a>
+<a className="md-button" href="/sleepkit/models/">Choose an architecture</a>
+
+## Tutorials and experiments
+
+Train a sleep-detection model from wrist IMU data in the saved notebook, or explore how feature and model choices affect sleep staging.
+
+<a className="md-button" href="/sleepkit/guides/train-detect-model/">Detection training notebook</a>
+<a className="md-button" href="/sleepkit/guides/stage-ablation/">Staging ablation study</a>
diff --git a/docs/guides/stage-ablation.md b/astro-site/src/content/docs/guides/stage-ablation.mdx
similarity index 81%
rename from docs/guides/stage-ablation.md
rename to astro-site/src/content/docs/guides/stage-ablation.mdx
index 2ffa5c9..a158186 100644
--- a/docs/guides/stage-ablation.md
+++ b/astro-site/src/content/docs/guides/stage-ablation.mdx
@@ -1,5 +1,7 @@
-# Sleep Staging Ablation Studies
-
+---
+title: "Sleep Staging Ablation Studies"
+description: "In the following, we perform ablation studies to investigate the impact of different design choices on the performance of the model. All ablation studies are…"
+---
 
 ## Overview
 
@@ -12,7 +14,7 @@ We first evaluate the impact of the temporal context- the number of time steps t
 A typical night sleep involves 4 or 5 sleep cycles each of which lasts around 90-120 minutes. By providing the model with nearly an entire cycle allows the model to learn the patterns associated with each stage. However, providing the model with more than one cycle does not provide additional benefit. This is likely due to the fact that the model is already able to learn the patterns associated with each stage within a single cycle.
 
 <div class="sk-plotly-graph-div">
---8<-- "assets/guides/stage/ablation-temporal.html"
+<iframe title="Interactive example" src="/sleepkit/assets/guides/stage/ablation-temporal.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
 </div>
 
 ---
@@ -22,7 +24,7 @@ A typical night sleep involves 4 or 5 sleep cycles each of which lasts around 90
 Another important design choice is the width (aka. # channels) of the network. To reduce the number of hyper-parameters, we provide a width multiplier to adjust number of channels in each layer relative to the baseline model. For example, a width multiplier of 0.5 means that the number of channels in each layer is half of the baseline model. The following plot shows the impact of the width multiplier on the validation loss. Increasing the width to 1.25 provides a slight 1.2% reduction in loss but requires 47% more FLOPS. As we continue to increase the width, the loss largely plateaus while the FLOPS and memory requirements increase dramatically. On the other hand, decreasing the width below 1 causes a stark increase in loss.
 
 <div class="sk-plotly-graph-div">
---8<-- "assets/guides/stage/ablation-width.html"
+<iframe title="Interactive example" src="/sleepkit/assets/guides/stage/ablation-width.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
 </div>
 
 ---
@@ -32,7 +34,7 @@ Another important design choice is the width (aka. # channels) of the network. T
 We further investigate the impact on kernel size on the validation loss. Traditionally, computer vision tasks leveraged 3x3 filter sizes. However, recent works have shown that larger filter sizes can provide better performance especially in 1D time-series by providing larger receptive fields. The plot below shows the impact of the kernel size on the validation loss. Using a filter length of 5 provides a significant reduction in loss while requiring 10% increase in FLOPS and 1K additional parameters. Beyond a filter length of 5, the loss plateaus while the FLOPS and memory requirements increase.
 
 <div class="sk-plotly-graph-div">
---8<-- "assets/guides/stage/ablation-kernelsize.html"
+<iframe title="Interactive example" src="/sleepkit/assets/guides/stage/ablation-kernelsize.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
 </div>
 
 ---
@@ -42,7 +44,7 @@ We further investigate the impact on kernel size on the validation loss. Traditi
 In the following ablation, we evaluate the impact of the squeeze-and-excitation (SE) ratio. As background, the SE block "adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels." The SE reduction ratio is a hyperparameter that controls the amount channel compression within the SE block. When the SE block is utilized, the reduction ratio has negligible impact on the computational and memory cost. The following plot shows the impact on validation loss for different SE reduction ratios. NOTE: `ratio=0` corresponds to no SE block. We see that using an SE ratio of 4 results in the lowest validation loss. This provides a 16% reduction in loss while requiring only 3K additional parameters versus no SE blocks.
 
 <div class="sk-plotly-graph-div">
---8<-- "assets/guides/stage/ablation-se-ratio.html"
+<iframe title="Interactive example" src="/sleepkit/assets/guides/stage/ablation-se-ratio.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
 </div>
 
 ---
@@ -51,9 +53,8 @@ In the following ablation, we evaluate the impact of the squeeze-and-excitation
 
 Taking inspiration from WaveNet, we evaluate the impact of dilated convolutions on the validation loss. By utilizing increasing dilation rates, we are able to increase the temporal context without increasing the number of parameters nor computation. For example, using a dilation rate of 4 with a kernel length of 5 gives a temporal resolution of 20 time steps compared to 5 time steps for a standard convolution. The following bar chart shows the impact of using dilation convolutions on the validation loss. In the case with dilation, dilation rates of 1, 2, 4, and 8 are used. By adding dilation the model loss decreases by 23% without any increase on memory or computation footprint.
 
-
 <div class="sk-plotly-graph-div">
---8<-- "assets/guides/stage/ablation-dilation.html"
+<iframe title="Interactive example" src="/sleepkit/assets/guides/stage/ablation-dilation.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
 </div>
 
 ---
diff --git a/docs/huggingface-artifacts.md b/astro-site/src/content/docs/huggingface-artifacts.mdx
similarity index 94%
rename from docs/huggingface-artifacts.md
rename to astro-site/src/content/docs/huggingface-artifacts.mdx
index 1875ed5..e7e26ce 100644
--- a/docs/huggingface-artifacts.md
+++ b/astro-site/src/content/docs/huggingface-artifacts.mdx
@@ -1,4 +1,7 @@
-# Publish an existing baseline to Hugging Face
+---
+title: "Publish an existing baseline to Hugging Face"
+description: "The additive sleepkit-artifacts CLI packages existing files without training or conversion. The existing sleepkit CLI is unchanged. The first reviewed adapter…"
+---
 
 The additive `sleepkit-artifacts` CLI packages existing files without training or
 conversion. The existing `sleepkit` CLI is unchanged. The first reviewed adapter
@@ -19,7 +22,7 @@ does not change those dependencies or upgrade TensorFlow.
 
 For optional synthetic runtime checks in a separate environment:
 
-```sh
+```sh title="Terminal"
 python -m venv /tmp/sleepkit-artifacts-env
 . /tmp/sleepkit-artifacts-env/bin/activate
 python -m pip install 'numpy>=2.1,<3' 'ai-edge-litert>=2.2,<3'
@@ -29,7 +32,7 @@ Obtain the three historical source files (the adapter verifies their pinned
 SHA-256 hashes before staging). Source files and generated bundles belong outside
 the repository:
 
-```sh
+```sh title="Terminal"
 mkdir -p /tmp/sd-2-source
 for file in model.tflite metrics.json configuration.json; do
   curl --fail --location \
@@ -76,10 +79,10 @@ These issues are documented in the model card and remain open for the next recip
 
 ## Stage a licensed release from an experiment
 
-Once the [per-model license decision](model-licensing-policy.md) is established,
+Once the [per-model license decision](/sleepkit/model-licensing-policy/) is established,
 promote the verified experiment into a new release directory:
 
-```sh
+```sh title="Terminal"
 python -m sleepkit.artifacts stage-release /path/to/experiment/bundle /path/to/release \
   --license-file /path/to/chosen/LICENSE --license-id bsd-3-clause \
   --card-body /path/to/release-card.md --decision-file /path/to/release-decision.md \
@@ -114,7 +117,7 @@ maintainer's decision; the tool cannot establish rights or approve publication.
 
 The same operation is available as ordinary Python:
 
-```python
+```python title="Python example"
 from pathlib import Path
 from sleepkit.artifacts import stage_release
 
@@ -150,7 +153,7 @@ or supply `--license-file PATH` and `--license-id HF_LICENSE_ID` when staging a
 historical baseline. Include required attribution and retain upstream notices. Then install `huggingface-hub>=1,<2`,
 authenticate using its standard token mechanism, and explicitly invoke:
 
-```sh
+```sh title="Terminal"
 python -m sleepkit.artifacts publish /tmp/licensed-sd-2-bundle \
   AmbiqAI/sleepkit-sd-2-tcn-sm --profile runnable --upload
 ```
@@ -164,7 +167,7 @@ it does not provision a hosted inference endpoint.
 
 Use the returned immutable revision for download and verification:
 
-```python
+```python title="Python example"
 import shutil
 from sleepkit.artifacts.hub import download_bundle
 from sleepkit.artifacts.runtime import replay_bundle
@@ -187,7 +190,7 @@ card conventions.
 
 ## Reuse from future recipes
 
-```python
+```python title="Python example"
 from pathlib import Path
 from sleepkit.artifacts import Artifact, TensorSpec, stage_bundle
 
@@ -214,7 +217,7 @@ The native Python detection recipe now integrates preprocessing, training, evalu
 and export. Next, exercise a deliberately different recipe before promoting shared
 blocks into a broader API; keep the legacy CLI available during this migration.
 
-The [membership int8 release preparation](detection-int8.md) now stages a new-model
+The [membership int8 release preparation](/sleepkit/detection-int8/) now stages a new-model
 bundle with preserved float/checkpoint artifacts, train-only calibration evidence,
 matched evaluation, and supported raw-sensor inference for both TFLite models.
 It can be validated and inspected through the same Hub dry-run interface. Model
diff --git a/docs/index.md b/astro-site/src/content/docs/index.mdx
similarity index 54%
rename from docs/index.md
rename to astro-site/src/content/docs/index.mdx
index 9ee6a2d..263d595 100644
--- a/docs/index.md
+++ b/astro-site/src/content/docs/index.mdx
@@ -1,10 +1,11 @@
-# From sleep signals to Edge AI
-
-<div class="sleepkit-intro">
+---
+title: "From sleep signals to Edge AI"
+description: "AI development kit for sleep monitoring on Ambiq devices."
+hero:
+  tagline: "sleepKIT is a Python development kit for building sleep-monitoring AI for Ambiq devices. Turn sensor recordings into features, train and evaluate models, and prepare them for deployment at the edge."
+---
 
-sleepKIT is a Python development kit for building sleep-monitoring AI for Ambiq devices. Turn sensor recordings into features, train and evaluate models, and prepare them for deployment at the edge.
-
-</div>
+import { Tabs, TabItem } from '@astrojs/starlight/components';
 
 ## A starting point for your sleep-monitoring application
 
@@ -39,7 +40,7 @@ Start with a built-in task, or extend the same workflow with your own training,
 </a>
 </div>
 
-Code and Ambiq-authored documentation use BSD-3-Clause, except separately licensed material. Model weights and datasets have [their own terms](model-licensing-policy.md).
+Code and Ambiq-authored documentation use BSD-3-Clause, except separately licensed material. Model weights and datasets have [their own terms](/sleepkit/model-licensing-policy/).
 
 ## Installation
 
@@ -47,115 +48,122 @@ Use **uv** for a Python project, **uvx** to run the CLI in an isolated environme
 
 <div class="sleepkit-install">
 
-=== "uv project"
+<Tabs>
+<TabItem label="uv project">
 
-    Create a project with Python 3.12, then add sleepKIT. In an existing uv project, start with `uv add sleepkit`.
+Create a project with Python 3.12, then add sleepKIT. In an existing uv project, start with `uv add sleepkit`.
 
-    ```bash
-    uv init --python 3.12 my-sleep-project
-    cd my-sleep-project
-    uv add sleepkit
-    uv run sleepkit --help
-    ```
+```bash title="Terminal"
+uv init --python 3.12 my-sleep-project
+cd my-sleep-project
+uv add sleepkit
+uv run sleepkit --help
+```
 
-=== "uvx"
+</TabItem>
+<TabItem label="uvx">
 
-    Run the CLI without adding sleepKIT to a project. The first invocation downloads sleepKIT and its dependencies into an isolated environment.
+Run the CLI without adding sleepKIT to a project. The first invocation downloads sleepKIT and its dependencies into an isolated environment.
 
-    ```bash
-    uvx --python 3.12 sleepkit --help
-    ```
+```bash title="Terminal"
+uvx --python 3.12 sleepkit --help
+```
 
-    Use a project installation for Python imports and notebooks.
+Use a project installation for Python imports and notebooks.
 
-=== "pipx"
+</TabItem>
+<TabItem label="pipx">
 
-    Install the CLI in its own environment. This command uses an installed Python 3.12 interpreter.
+Install the CLI in its own environment. This command uses an installed Python 3.12 interpreter.
 
-    ```bash
-    pipx install --python python3.12 sleepkit
-    sleepkit --help
-    ```
+```bash title="Terminal"
+pipx install --python python3.12 sleepkit
+sleepkit --help
+```
 
-    If the command is not on your PATH, run `pipx ensurepath` and reopen your terminal. Use a project installation for Python imports and notebooks.
+If the command is not on your PATH, run `pipx ensurepath` and reopen your terminal. Use a project installation for Python imports and notebooks.
 
-=== "pip"
+</TabItem>
+<TabItem label="pip">
 
-    Install into an activated virtual environment.
+Install into an activated virtual environment.
 
-    ```bash
-    python -m pip install sleepkit
-    sleepkit --help
-    ```
+```bash title="Terminal"
+python -m pip install sleepkit
+sleepkit --help
+```
 
-=== "Git clone"
+</TabItem>
+<TabItem label="Git clone">
 
-    Work with the repository source and its development dependencies.
+Work with the repository source and its development dependencies.
 
-    ```bash
-    git clone https://github.com/AmbiqAI/sleepkit.git
-    cd sleepkit
-    uv sync --python 3.12
-    uv run sleepkit --help
-    ```
+```bash title="Terminal"
+git clone https://github.com/AmbiqAI/sleepkit.git
+cd sleepkit
+uv sync --python 3.12
+uv run sleepkit --help
+```
 
-</div>
+</TabItem>
+</Tabs>
 
-Need the package manager first? See the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/) or [pipx installation guide](https://pipx.pypa.io/stable/installation/). The [Quickstart](./quickstart.md) covers configuration and your first workflow.
+</div>
 
+Need the package manager first? See the [uv installation guide](https://docs.astral.sh/uv/getting-started/installation/) or [pipx installation guide](https://pipx.pypa.io/stable/installation/). The [Quickstart](/sleepkit/quickstart/) covers configuration and your first workflow.
 
 ---
 
 ## Usage
 
-__sleepKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced development. In both forms, sleepKIT exposes a number of modes and tasks outlined below. In addition, by leveraging highly-customizable configurations, sleepKIT can be used to create custom workflows for a given application with minimal coding. Refer to the [Quickstart](./quickstart.md) to quickly get up and running in minutes.
+__sleepKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced development. In both forms, sleepKIT exposes a number of modes and tasks outlined below. In addition, by leveraging highly-customizable configurations, sleepKIT can be used to create custom workflows for a given application with minimal coding. Refer to the [Quickstart](/sleepkit/quickstart/) to quickly get up and running in minutes.
 
 ---
 
 ## Modes
 
-The __ADK__ provides a number of [modes](./modes/index.md) that can be invoked for a given task. These modes can be accessed via the CLI or directly within the Python package. Each mode is accompanied by a set of [task parameters](./modes/configuration.md) that can be customized to fit the user's needs.
+The __ADK__ provides a number of [modes](/sleepkit/modes/) that can be invoked for a given task. These modes can be accessed via the CLI or directly within the Python package. Each mode is accompanied by a set of [task parameters](/sleepkit/modes/configuration/) that can be customized to fit the user's needs.
 
-- **[Download](./modes/download.md)**: Download specified datasets
-- **[Feature](./features/index.md)**: Generate features from datasets
-- **[Train](./modes/train.md)**: Train a model for specified task and feature set
-- **[Evaluate](./modes/evaluate.md)**: Evaluate a model for specified task and feature set
-- **[Export](./modes/export.md)**: Export a trained model to TensorFlow Lite and TFLM
-- **[Demo](./modes/demo.md)**: Run task-level demo on PC or remotely on Ambiq EVB
+- **[Download](/sleepkit/modes/download/)**: Download specified datasets
+- **[Feature](/sleepkit/features/)**: Generate features from datasets
+- **[Train](/sleepkit/modes/train/)**: Train a model for specified task and feature set
+- **[Evaluate](/sleepkit/modes/evaluate/)**: Evaluate a model for specified task and feature set
+- **[Export](/sleepkit/modes/export/)**: Export a trained model to TensorFlow Lite and TFLM
+- **[Demo](/sleepkit/modes/demo/)**: Run task-level demo on PC or remotely on Ambiq EVB
 
 ---
 
 ## Datasets
 
-__sleepKIT__ includes several open-source datasets via the __dataset factory__. Each dataset has a corresponding Python class to aid in downloading and extracting the data. The datasets are used to generate feature sets that are then used to train and evaluate the models. Check out the [Datasets Guide](./datasets/index.md) to learn more about the available datasets along with their corresponding licenses and limitations.
+__sleepKIT__ includes several open-source datasets via the __dataset factory__. Each dataset has a corresponding Python class to aid in downloading and extracting the data. The datasets are used to generate feature sets that are then used to train and evaluate the models. Check out the [Datasets Guide](/sleepkit/datasets/) to learn more about the available datasets along with their corresponding licenses and limitations.
 
-* **[MESA](./datasets/mesa.md)**: A longitudinal investigation of factors associated with the development of subclinical cardiovascular disease and the progression of subclinical to clinical cardiovascular disease in 6,814 black, white, Hispanic, and Chinese
-* **[CMIDSS](./datasets/cmidss.md)**: The Child Mind Institute - Detect Sleep States (CMIDSS) dataset comprises 300 subjects with over 500 multi-day recordings of wrist-worn accelerometer data annotated with two event types: onset, the beginning of sleep, and wakeup, the end of sleep.
-* **[YSYW](./datasets/ysyw.md)**: A total of 1,983 PSG recordings were provided by the Massachusetts General Hospital’s (MGH) Sleep Lab in the Sleep Division together with the Computational Clinical Neurophysiology Laboratory, and the Clinical Data Ani- mation Center.
-* **[STAGES](./datasets/stages.md)**: The Stanford Technology Analytics and Genomics in Sleep (STAGES) study is a prospective cross-sectional, multi-site study involving 20 data collection sites from six centers including Stanford University, Bogan Sleep Consulting, Geisinger Health, Mayo Clinic, MedSleep, and St. Luke's Hospital.
+* **[MESA](/sleepkit/datasets/mesa/)**: A longitudinal investigation of factors associated with the development of subclinical cardiovascular disease and the progression of subclinical to clinical cardiovascular disease in 6,814 black, white, Hispanic, and Chinese
+* **[CMIDSS](/sleepkit/datasets/cmidss/)**: The Child Mind Institute - Detect Sleep States (CMIDSS) dataset comprises 300 subjects with over 500 multi-day recordings of wrist-worn accelerometer data annotated with two event types: onset, the beginning of sleep, and wakeup, the end of sleep.
+* **[YSYW](/sleepkit/datasets/ysyw/)**: A total of 1,983 PSG recordings were provided by the Massachusetts General Hospital’s (MGH) Sleep Lab in the Sleep Division together with the Computational Clinical Neurophysiology Laboratory, and the Clinical Data Ani- mation Center.
+* **[STAGES](/sleepkit/datasets/stages/)**: The Stanford Technology Analytics and Genomics in Sleep (STAGES) study is a prospective cross-sectional, multi-site study involving 20 data collection sites from six centers including Stanford University, Bogan Sleep Consulting, Geisinger Health, Mayo Clinic, MedSleep, and St. Luke's Hospital.
 
 ---
 
 ## Models
 
-The __ADK__ provides a variety of model architectures geared towards efficient, real-time edge applications. These models are provided by Ambiq's [helia-edge](https://ambiqai.github.io/helia-edge/) and expose a set of parameters that can be used to fully customize the network for a given application. In addition, sleepKIT includes a model factory, [ModelFactory](./models/index.md#model-factory), to register current models as well as allow new custom architectures to be added. Check out the [Models Guide](./models/index.md) to learn more about the available network architectures and model factory.
+The __ADK__ provides a variety of model architectures geared towards efficient, real-time edge applications. These models are provided by Ambiq's [helia-edge](https://ambiqai.github.io/helia-edge/) and expose a set of parameters that can be used to fully customize the network for a given application. In addition, sleepKIT includes a model factory, [ModelFactory](/sleepkit/models/#model-factory), to register current models as well as allow new custom architectures to be added. Check out the [Models Guide](/sleepkit/models/) to learn more about the available network architectures and model factory.
 
 ---
 
 ## Features
 
-The __ADK__ provides a __feature store__ that allows you to easily create and extract features from the given datasets. The feature store includes a number of feature sets used to train the included model zoo. Each feature set exposes a number of high-level parameters that can be used to customize the feature extraction process for a given application. These parameters can be set as part of the configuration accessible via the CLI and Python package. Check out the [Features Guide](./features/index.md) to learn more about the available feature set generators.
+The __ADK__ provides a __feature store__ that allows you to easily create and extract features from the given datasets. The feature store includes a number of feature sets used to train the included model zoo. Each feature set exposes a number of high-level parameters that can be used to customize the feature extraction process for a given application. These parameters can be set as part of the configuration accessible via the CLI and Python package. Check out the [Features Guide](/sleepkit/features/) to learn more about the available feature set generators.
 
 ---
 
 ## Model Zoo
 
-A number of pre-trained models are available for each task. These models are trained on a variety of datasets and are optimized for deployment on Ambiq's ultra-low power SoCs. In addition to providing links to download the models, __sleepKIT__ provides the corresponding configuration files and performance metrics. The configuration files allow you to easily recreate the models or use them as a starting point for custom solutions. Furthermore, the performance metrics provide insights into the model's accuracy, precision, recall, and F1 score. For a number of the models, we provide experimental and ablation studies to showcase the impact of various design choices. Check out the [Model Zoo](./zoo/index.md) to learn more about the available models and their corresponding performance metrics.
+A number of pre-trained models are available for each task. These models are trained on a variety of datasets and are optimized for deployment on Ambiq's ultra-low power SoCs. In addition to providing links to download the models, __sleepKIT__ provides the corresponding configuration files and performance metrics. The configuration files allow you to easily recreate the models or use them as a starting point for custom solutions. Furthermore, the performance metrics provide insights into the model's accuracy, precision, recall, and F1 score. For a number of the models, we provide experimental and ablation studies to showcase the impact of various design choices. Check out the [Model Zoo](/sleepkit/zoo/) to learn more about the available models and their corresponding performance metrics.
 
 ---
 
-## [Guides](./guides/index.md)
+## [Guides](/sleepkit/guides/)
 
-Checkout the [Guides](./guides/index.md) to see detailed examples and tutorials on how to use sleepKIT for a variety of tasks. The guides provide step-by-step instructions on how to train, evaluate, and deploy models for a given task. In addition, the guides provide insights into the design choices and performance metrics for the models. The guides are designed to help you get up and running quickly and to provide a deeper understanding of the capabilities provided by sleepKIT.
+Checkout the [Guides](/sleepkit/guides/) to see detailed examples and tutorials on how to use sleepKIT for a variety of tasks. The guides provide step-by-step instructions on how to train, evaluate, and deploy models for a given task. In addition, the guides provide insights into the design choices and performance metrics for the models. The guides are designed to help you get up and running quickly and to provide a deeper understanding of the capabilities provided by sleepKIT.
 
 ---
diff --git a/docs/model-licensing-policy.md b/astro-site/src/content/docs/model-licensing-policy.mdx
similarity index 96%
rename from docs/model-licensing-policy.md
rename to astro-site/src/content/docs/model-licensing-policy.mdx
index 365c2ef..22bc708 100644
--- a/docs/model-licensing-policy.md
+++ b/astro-site/src/content/docs/model-licensing-policy.mdx
@@ -1,4 +1,7 @@
-# Code, site and model licensing
+---
+title: "Code, site and model licensing"
+description: "sleepKIT's code, recipes and Ambiq-authored documentation/site content use BSD-3-Clause, except material carrying a separate notice. Third-party assets,…"
+---
 
 sleepKIT's code, recipes and Ambiq-authored documentation/site content use
 **BSD-3-Clause**, except material carrying a separate notice. Third-party assets,
@@ -21,7 +24,7 @@ not retract rights granted in previous releases or relicense historical artifact
 | Custom Ambiq device model license | Upstream rights permit the restriction and the release explicitly opts in | Proposed terms allow Ambiq deployment and host development/evaluation | Ambiq embedded execution; general-purpose host evaluation allowed |
 | Dataset-compatible research license | Dataset or parent-model terms constrain the release | Follow the exact upstream terms; NC is not commercial permission | No extra restriction that conflicts with those terms |
 
-The [custom Ambiq terms](licenses/ambiq-device-model-license-draft.md) are a draft,
+The custom Ambiq terms are a draft,
 not an active license. They are intended for otherwise compatible models, not an
 addendum to a Creative Commons license. Each release chooses terms explicitly;
 there is no default hardware restriction on every sleepKIT model.
@@ -97,7 +100,7 @@ restricted weights. The publisher's license-presence check is not a rights revie
 Status on 2026-09-20: **staged locally; license candidate identified; not published**.
 This is the five-epoch nightly-period-membership experiment and its fixed int8
 conversion, not clinical sleep/wake classification. See the
-[conversion evidence and file inventory](detection-int8.md).
+[conversion evidence and file inventory](/sleepkit/detection-int8/).
 
 | Decision field | Current evidence |
 | --- | --- |
diff --git a/docs/models/byom.md b/astro-site/src/content/docs/models/byom.mdx
similarity index 87%
rename from docs/models/byom.md
rename to astro-site/src/content/docs/models/byom.mdx
index 6964077..3687b9f 100644
--- a/docs/models/byom.md
+++ b/astro-site/src/content/docs/models/byom.mdx
@@ -1,4 +1,7 @@
-# Bring-Your-Own-Model (BYOM)
+---
+title: "Bring-Your-Own-Model (BYOM)"
+description: "The model factory can be extended to include custom models. This is useful when you have a custom model architecture that you would like to use for training.…"
+---
 
 The model factory can be extended to include custom models. This is useful when you have a custom model architecture that you would like to use for training. The custom model can be registered with the model factory by defining a custom model function and registering it with the `sk.ModelFactory`.
 
@@ -6,7 +9,7 @@ The model factory can be extended to include custom models. This is useful when
 
 1. **Create a Model**: Define a new model function that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
 
     import keras
     import sleepkit as sk
@@ -30,13 +33,13 @@ The model factory can be extended to include custom models. This is useful when
 
 2. **Register the Model**: Register the new model function with the `sk.ModelFactory` by calling the `register` method. This method takes the model name and the callable as arguments.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     sk.ModelFactory.register("custom-model", custom_model_from_params)
     ```
 
 3. **Use the Model**: The new model can now be used with the `sk.ModelFactory` to perform various operations such as downloading and generating data.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     inputs = keras.Input(shape=(100,))
     model = sk.ModelFactory.get("custom-model")(
         inputs=inputs,
@@ -57,7 +60,7 @@ The model factory can be extended to include custom models. This is useful when
 
 Rather than using a dictionary to define the model parameters, you can define a custom dataclass or [Pydantic](https://pydantic-docs.helpmanual.io/) model to enforce type checking and provide better documentation.
 
-```py linenums="1"
+```py title="Python example" linenums="1"
 from pydantic import BaseModel
 
 class CustomLayerParams(BaseModel):
diff --git a/docs/models/index.md b/astro-site/src/content/docs/models/index.mdx
similarity index 90%
rename from docs/models/index.md
rename to astro-site/src/content/docs/models/index.mdx
index 3442e8d..778ae03 100644
--- a/docs/models/index.md
+++ b/astro-site/src/content/docs/models/index.mdx
@@ -1,4 +1,7 @@
-# :material-graph-outline: Models
+---
+title: "Models"
+description: "sleepKIT provides a number of model architectures that can be used for training sleep-monitoring tasks. While a number of off-the-shelf models exist, they are…"
+---
 
 sleepKIT provides a number of model architectures that can be used for training __sleep-monitoring tasks__. While a number of off-the-shelf models exist, they are often not efficient nor optimized for real-time, edge applications. To address this, sleepKIT provides a model factory that allows you to easily create and train customized models via [helia-edge](https://ambiqai.github.io/helia-edge/). `helia-edge` includes a growing number of state-of-the-art models that can be easily configured and trained using high-level parameters. The models are designed to be efficient and well-suited for real-time, edge applications. Most of the models are based on state-of-the-art architectures that have been modified to allow for more fine-grain customization. In addition, the models support 1D variants to allow for training on time-series data. Please check [helia-edge](https://ambiqai.github.io/helia-edge/) for list of available models and their configurations.
 
@@ -13,7 +16,7 @@ sleepKIT provides a number of model architectures that can be used for training
 - **[MobileOne](https://ambiqai.github.io/helia-edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`)
 - **[ResNet](https://ambiqai.github.io/helia-edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`)
 - **Composer**: Compose an architecture through the registered `composer` builder.
-* **[Bring-Your-Own-Model](./byom.md)**: Add a custom model architecture to sleepKIT.
+* **[Bring-Your-Own-Model](/sleepkit/models/byom/)**: Add a custom model architecture to sleepKIT.
 
 ---
 
@@ -35,11 +38,11 @@ The model factory provides the following methods:
 
 ### Defining a model in configuration file
 
-A model can be created when invoking a command via the CLI by setting [architecture](../modes/configuration.md#taskparams) in the configuration file. The task will use the supplied name to get the registered model and instantiate with the provided parameters.
+A model can be created when invoking a command via the CLI by setting [architecture](/sleepkit/modes/configuration/#taskparams) in the configuration file. The task will use the supplied name to get the registered model and instantiate with the provided parameters.
 
 Merge this architecture fragment into your task configuration. It does not include dataset, feature or training settings.
 
-```json fragment
+```json title="Configuration fragment"
 {
     "architecture": {
         "name": "tcn",
@@ -64,16 +67,15 @@ Merge this architecture fragment into your task configuration. It does not inclu
 
 After completing the task configuration, train with:
 
-```bash
+```bash title="Terminal"
 sleepkit --mode train --task stage --config configuration.json
 ```
 
-
 ### Defining a model in code
 
 Alternatively, the model can be created directly in code using the following snippet:
 
-```py linenums="1"
+```py title="Python example" linenums="1"
 
 import keras
 import sleepkit as sk
diff --git a/docs/modes/configuration.md b/astro-site/src/content/docs/modes/configuration.mdx
similarity index 88%
rename from docs/modes/configuration.md
rename to astro-site/src/content/docs/modes/configuration.mdx
index 24c42c7..40d40e8 100644
--- a/docs/modes/configuration.md
+++ b/astro-site/src/content/docs/modes/configuration.mdx
@@ -1,4 +1,7 @@
-# :material-code-json: Configuration Parameters
+---
+title: "Configuration Parameters"
+description: "For each mode, common configuration parameters, TaskParams, are required to run the task. These parameters are used to define the task, datasets, model, and…"
+---
 
 For each mode, common configuration parameters, [TaskParams](#taskparams), are required to run the task. These parameters are used to define the task, datasets, model, and other settings. Rather than defining separate configuration files for each mode, a single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.
 
@@ -23,9 +26,9 @@ Named parameters are used to provide custom parameters for a given object or cal
 | Argument | Type | Opt/Req | Default | Description |
 | --- | --- | --- | --- | --- |
 | name | str | Required |  | Named parameters name |
-| params | dict[str, Any] | Optional | {} | Named parameters |
+| params | dict[str, Any] | Optional | \{\} | Named parameters |
 
-```py linenums="1"
+```py title="Python example" linenums="1"
 
 import sleepkit as sk
 
@@ -61,14 +64,11 @@ params = sk.TaskParams(
 | mask_key | str | Optional | mask | Mask key |
 | feat_cols | list[str]\|None | Optional | None | Feature columns |
 | save_path | Path | Optional | `tempfile.gettempdir` | Save path |
-| params | dict[str, Any] | Optional | {} | Feature Parameters |
-
-
+| params | dict[str, Any] | Optional | \{\} | Feature Parameters |
 
 ## TaskParams
 
-These parameters are supplied to a [Task](../tasks/index.md) when running a given mode such as `train`, `evaluate`, `export`, or `demo`. A single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.
-
+These parameters are supplied to a [Task](/sleepkit/tasks/) when running a given mode such as `train`, `evaluate`, `export`, or `demo`. A single configuration object is used to simplify configuration files and heavy re-use of parameters between modes.
 
 | Argument | Type | Opt/Req | Default | Description | Mode |
 | --- | --- | --- | --- | --- | --- |
@@ -78,7 +78,7 @@ These parameters are supplied to a [Task](../tasks/index.md) when running a give
 | datasets | list[NamedParams] | Optional | [] | Datasets | All |
 | dataset_weights | list[float]\|None | Optional | None | Dataset weights | All |
 | force_download | bool | Optional | False | Force download dataset- overriding existing files | download |
-| feature | FeatureParams | Optional | {} | Feature configuration | feature |
+| feature | FeatureParams | Optional | \{\} | Feature configuration | feature |
 | sampling_rate | float | Optional | 250 | Target sampling rate (Hz) | feature |
 | frame_size | int | Optional | 1250 | Frame size in samples | feature |
 | samples_per_subject | int\|list[int] | Optional | 1000 | Number train samples per subject | All |
@@ -87,7 +87,7 @@ These parameters are supplied to a [Task](../tasks/index.md) when running a give
 | preprocesses | list[NamedParams] | Optional | [] | Preprocesses | All |
 | augmentations | list[NamedParams] | Optional | [] | Augmentations | All |
 | num_classes | int | Optional | 1 | # of classes | All |
-| class_map | dict[int, int] | Optional | {1: 1} | Class/label mapping | All |
+| class_map | dict[int, int] | Optional | \{1: 1\} | Class/label mapping | All |
 | class_names | list[str]\|None | Optional | None | Class names | All |
 | train_subjects | float\|None | Optional | None | # or proportion of subjects for training | All |
 | val_subjects | float\|None | Optional | None | # or proportion of subjects for validation | All |
@@ -101,7 +101,7 @@ These parameters are supplied to a [Task](../tasks/index.md) when running a give
 | model_file | Path\|None | Optional | None | Path to load/save model file (.keras) | All |
 | use_logits | bool | Optional | True | Use logits output or softmax | All |
 | weights_file | Path\|None | Optional | None | Path to a checkpoint weights to load/save | All |
-| quantization | QuantizationParams | Optional | {} | Quantization parameters | train, evaluate, export, demo |
+| quantization | QuantizationParams | Optional | \{\} | Quantization parameters | train, evaluate, export, demo |
 | lr_rate | float | Optional | 1e-3 | Learning rate | train |
 | lr_cycles | int | Optional | 3 | Number of learning rate cycles | train |
 | lr_decay | float | Optional | 0.9 | Learning rate decay | train |
diff --git a/docs/modes/demo.md b/astro-site/src/content/docs/modes/demo.mdx
similarity index 80%
rename from docs/modes/demo.md
rename to astro-site/src/content/docs/modes/demo.mdx
index 8ed3189..df2d51c 100644
--- a/docs/modes/demo.md
+++ b/astro-site/src/content/docs/modes/demo.mdx
@@ -1,6 +1,11 @@
-# :material-hexagon-multiple: Task-Level Model Demo
+---
+title: "Task-Level Model Demo"
+description: "Each task in sleepKIT has a corresponding demo mode that allows you to run a task-level demonstration using the specified backend inference engine (e.g. PC or…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
 
-## Introduction 
+## Introduction
 
 Each task in sleepKIT has a corresponding demo mode that allows you to run a task-level demonstration using the specified backend inference engine (e.g. PC or EVB). This is useful to showcase the model's performance in real-time and to verify its accuracy in a real-world scenario. Similar to other modes, the demo can be invoked either via CLI or within `sleepkit` python package. At a high level, the demo mode performs the following actions based on the provided configuration parameters:
 
@@ -82,7 +87,7 @@ Similar to datasets, tasks, and models, the demo mode can be customized to use y
 
 1. **Create a Backend**: Define a new backend class that inherits from the `sk.InferenceBackend` base class and implements the required methods. This skeleton must be implemented before use.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
     import numpy.typing as npt
 
@@ -108,14 +113,14 @@ Similar to datasets, tasks, and models, the demo mode can be customized to use y
 
 2. **Register the Backend**: Register the new backend with the `sk.BackendFactory` by calling the `register` method. This method takes the backend name and the backend class as arguments.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
     sk.BackendFactory.register("custom", CustomBackend)
     ```
 
 3. **Use the Backend**: The new backend can now be used by setting the `backend` flag in the demo configuration settings.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
 
     from pathlib import Path
@@ -130,7 +135,7 @@ Similar to datasets, tasks, and models, the demo mode can be customized to use y
 
     _OR_ by creating the backend directly:
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
 
     from pathlib import Path
@@ -144,35 +149,40 @@ Similar to datasets, tasks, and models, the demo mode can be customized to use y
 
 ---
 
-## Usage 
+## Usage
 
 The following is an example of a task-level demo report for the sleep staging task.
 
-=== "CLI"
+<Tabs>
+<TabItem label="CLI">
 
-    ```bash
-    sleepkit -m demo -t stage -c ./configuration.json
-    ```
+```bash title="Terminal"
+sleepkit -m demo -t stage -c ./configuration.json
+```
 
-=== "Python"
+</TabItem>
+<TabItem label="Python">
 
-    ```py linenums="1"
-    from pathlib import Path
-    import sleepkit as sk
+```py title="Python example" linenums="1"
+from pathlib import Path
+import sleepkit as sk
 
-    task = sk.TaskFactory.get("stage")
-    params = sk.TaskParams.model_validate_json(Path("configuration.json").read_text())
-    task.demo(params)
-    ```
+task = sk.TaskFactory.get("stage")
+params = sk.TaskParams.model_validate_json(Path("configuration.json").read_text())
+task.demo(params)
+```
+
+</TabItem>
+</Tabs>
 
 <div class="sk-plotly-graph-div">
---8<-- "assets/zoo/stage/sleep-stage-demo.html"
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/sleep-stage-demo.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
 </div>
 
 ---
 
-## Arguments 
+## Arguments
 
-Please refer to [TaskParams](../modes/configuration.md#taskparams) for the list of arguments that can be used with the `demo` command.
+Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) for the list of arguments that can be used with the `demo` command.
 
 ---
diff --git a/astro-site/src/content/docs/modes/download.mdx b/astro-site/src/content/docs/modes/download.mdx
new file mode 100644
index 0000000..3454870
--- /dev/null
+++ b/astro-site/src/content/docs/modes/download.mdx
@@ -0,0 +1,34 @@
+---
+title: "Download Datasets"
+description: "The download command is used to download all datasets specified. Please refer to Datasets for details on the available datasets. Additional datasets can be…"
+---
+
+The `download` command is used to download all datasets specified. Please refer to [Datasets](/sleepkit/datasets/) for details on the available datasets. Additional datasets can be added by creating a new dataset class and registering it with __sleepKIT__ dataset factory.
+
+## Usage
+
+### CLI
+
+Using the CLI, the `download` command can be used to download specified datasets in the configuration file or directly in the command line.
+
+```bash title="Terminal"
+sleepkit -m download -c '{"datasets": [{"name": "cmidss", "parameters": {"path": ".datatasets/cmidss"}}]}'
+```
+
+### Python
+
+In code, the `download` method of a dataset can be used to download the dataset.
+
+```py title="Python example" linenums="1"
+import sleepkit as sk
+
+ds = sk.DatasetFactory.get("cmidss")(path="./datasets/cmidss")
+ds.download()
+
+```
+
+## Arguments
+
+Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) for the list of arguments that can be used with the `download` command.
+
+---
diff --git a/astro-site/src/content/docs/modes/evaluate.mdx b/astro-site/src/content/docs/modes/evaluate.mdx
new file mode 100644
index 0000000..61e766b
--- /dev/null
+++ b/astro-site/src/content/docs/modes/evaluate.mdx
@@ -0,0 +1,293 @@
+---
+title: "Model Evaluation"
+description: "Evaluate mode is used to test the performance of the model on the reserved test set for the specified task. Similar to training, the routine can be customized…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+## Introduction
+
+Evaluate mode is used to test the performance of the model on the reserved test set for the specified task. Similar to training, the routine can be customized via CLI configuration file or by setting the parameters directly in the code. The evaluation process involves testing the model's performance on the test data to measure its accuracy, precision, recall, and F1 score. A number of results and metrics will be generated and saved to the `job_dir`.
+
+<div class="annotate">
+
+1. Load the configuration data (e.g. `configuration.json`)
+1. Load features (e.g. `FS-W-A-5`)
+1. Load the trained model (e.g. `model.keras`)
+1. Define the metrics (e.g. `accuracy`)
+1. Evaluate the model (e.g. `model.evaluate`)
+1. Generate evaluation report (e.g. `report.json`)
+
+</div>
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="modes-evaluate-1.json" download="/sleepkit/examples/modes-evaluate-1.json">
+
+```json title="modes-evaluate-1.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+```mermaid
+graph TD
+A("`Load
+configuration
+__TaskParams__
+`")
+B("`Load
+features
+__FeatureFactory__
+`")
+C("`Load trained
+__model__
+`")
+D("`Define
+__metrics__
+`")
+E("`Evaluate
+__model__
+`")
+F("`Generate
+__report__
+`")
+A ==> B
+B ==> C
+subgraph CF["Evaluate"]
+    C ==> D
+    D ==> E
+end
+E ==> F
+
+```
+
+---
+
+## Usage
+
+### CLI
+
+The following command will evaluate a detect model using the reference configuration.
+
+```bash title="Terminal"
+sleepkit --task detect --mode evaluate --config ./configuration.json
+```
+
+### Python
+
+The model can be evaluated using the following snippet:
+
+```py title="Python example" linenums="1"
+from pathlib import Path
+import sleepkit as sk
+
+task = sk.TaskFactory.get("detect")
+
+params = sk.TaskParams.model_validate_json(
+    Path("configuration.json").read_text()
+)
+
+task.evaluate(params)
+
+```
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="modes-evaluate-4.json" download="/sleepkit/examples/modes-evaluate-4.json">
+
+```json title="modes-evaluate-4.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+---
+
+## Arguments
+
+Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) for the list of arguments that can be used with the `evaluate` command.
+
+---
diff --git a/astro-site/src/content/docs/modes/export.mdx b/astro-site/src/content/docs/modes/export.mdx
new file mode 100644
index 0000000..a9c3be8
--- /dev/null
+++ b/astro-site/src/content/docs/modes/export.mdx
@@ -0,0 +1,297 @@
+---
+title: "Model Exporting"
+description: "Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+## Introduction
+
+Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file or by setting the `quantization` parameter in the code.
+
+<div class="annotate">
+
+1. Load the configuration data (e.g. `configuration.json`)
+1. Load the test data (e.g. `test.tfds`)
+1. Load the trained model (e.g. `model.keras`)
+1. Quantize the model (e.g. `16x8`)
+1. Convert the model (e.g. `TFL`, `TFLM`)
+1. Verify the models' outputs match
+1. Save artifacts (e.g. `model.tflite`)
+
+</div>
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="modes-export-1.json" download="/sleepkit/examples/modes-export-1.json">
+
+```json title="modes-export-1.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+```mermaid
+graph TD
+A("`Load
+configuration
+__TaskParams__
+`")
+B("`Load test
+data
+`")
+C("`Load trained
+__model__
+`")
+D("`Quantize
+__model__
+`")
+E("`Convert
+__model__
+`")
+F("`Verify
+__outputs__
+`")
+G("`Save
+__artifacts__
+`")
+A ==> B
+B ==> C
+subgraph CF["Export"]
+    C ==> D
+    D ==> E
+    E ==> F
+end
+F ==> G
+
+```
+
+---
+
+## Usage
+
+### CLI
+
+The following command will export a detect model using the reference configuration.
+
+```bash title="Terminal"
+sleepkit --task detect --mode export --config ./configuration.json
+```
+
+### Python
+
+The model can be exported using the following snippet:
+
+```py title="Python example" linenums="1"
+from pathlib import Path
+import sleepkit as sk
+
+task = sk.TaskFactory.get("detect")
+
+params = sk.TaskParams.model_validate_json(
+    Path("configuration.json").read_text()
+)
+
+task.export(params)
+
+```
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="modes-export-4.json" download="/sleepkit/examples/modes-export-4.json">
+
+```json title="modes-export-4.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+---
+
+## Arguments
+
+Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) for the list of arguments that can be used with the `export` command.
+
+---
diff --git a/docs/modes/index.md b/astro-site/src/content/docs/modes/index.mdx
similarity index 64%
rename from docs/modes/index.md
rename to astro-site/src/content/docs/modes/index.mdx
index 14fe04e..8db5e66 100644
--- a/docs/modes/index.md
+++ b/astro-site/src/content/docs/modes/index.mdx
@@ -1,4 +1,7 @@
-# Modes overview
+---
+title: "Modes overview"
+description: "Modes are the steps in a sleepKIT task: acquire data, generate features, train, evaluate, export, and run a demo. Select a mode with --mode and provide the…"
+---
 
 Modes are the steps in a sleepKIT task: acquire data, generate features, train, evaluate, export, and run a demo. Select a mode with `--mode` and provide the task configuration with `--config`.
 
@@ -6,12 +9,12 @@ Modes are the steps in a sleepKIT task: acquire data, generate features, train,
 
 | Mode | Purpose | Guide |
 | --- | --- | --- |
-| `download` | Fetch the datasets selected in your configuration. | [Download datasets](./download.md) |
-| `feature` | Generate features from your datasets. | [Feature generation](../features/index.md) |
-| `train` | Train the task model with the configured features and settings. | [Train a model](./train.md) |
-| `evaluate` | Evaluate the model on the configured test data. | [Evaluate a model](./evaluate.md) |
-| `export` | Convert the trained model for deployment. | [Export a model](./export.md) |
-| `demo` | Run a task-level demonstration with the configured backend. | [Run a demo](./demo.md) |
+| `download` | Fetch the datasets selected in your configuration. | [Download datasets](/sleepkit/modes/download/) |
+| `feature` | Generate features from your datasets. | [Feature generation](/sleepkit/features/) |
+| `train` | Train the task model with the configured features and settings. | [Train a model](/sleepkit/modes/train/) |
+| `evaluate` | Evaluate the model on the configured test data. | [Evaluate a model](/sleepkit/modes/evaluate/) |
+| `export` | Convert the trained model for deployment. | [Export a model](/sleepkit/modes/export/) |
+| `demo` | Run a task-level demonstration with the configured backend. | [Run a demo](/sleepkit/modes/demo/) |
 
 ## Run a step
 
@@ -25,4 +28,4 @@ Demo support depends on the task and backend. See the demo guide for setup and l
 
 ## Python workflow
 
-The Python interface exposes the same task operations for scripts and notebooks. See [Quickstart](../quickstart.md#use-sleepkit-with-python) for a configuration-driven example and [Configuration](./configuration.md) for parameter definitions.
+The Python interface exposes the same task operations for scripts and notebooks. See [Quickstart](/sleepkit/quickstart/#use-sleepkit-with-python) for a configuration-driven example and [Configuration](/sleepkit/modes/configuration/) for parameter definitions.
diff --git a/astro-site/src/content/docs/modes/train.mdx b/astro-site/src/content/docs/modes/train.mdx
new file mode 100644
index 0000000..7386be4
--- /dev/null
+++ b/astro-site/src/content/docs/modes/train.mdx
@@ -0,0 +1,308 @@
+---
+title: "Model Training"
+description: "Each task provides a mode to train a model on the specified features. The training mode can be invoked either via CLI or within sleepkit python package. At a…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+## Introduction
+
+Each task provides a mode to train a model on the specified features. The training mode can be invoked either via CLI or within `sleepkit` python package. At a high level, the training mode performs the following actions based on the provided configuration parameters:
+
+<div class="annotate">
+
+1. Load the configuration data (e.g. `configuration.json`)
+1. Load features (e.g. `FS-W-A-5`)
+1. Initialize custom model architecture (e.g. `tcn`)
+1. Define the metrics, loss, and optimizer (e.g. `accuracy`, `categorical_crossentropy`, `adam`)
+1. Train the model (e.g. `model.fit`)
+1. Save artifacts (e.g. `model.keras`)
+
+</div>
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="modes-train-1.json" download="/sleepkit/examples/modes-train-1.json">
+
+```json title="modes-train-1.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+<br/>
+
+```mermaid
+graph TD
+A("`Load
+configuration
+__TaskParams__
+`")
+B("`Load
+features
+__FeatureFactory__
+`")
+C("`Initialize
+model
+__ModelFactory__
+`")
+D("`Define
+_metrics_, _loss_,
+_optimizer_
+`")
+E("`Train
+__model__
+`")
+F("`Save
+__artifacts__
+`")
+A ==> B
+B ==> C
+subgraph "Model Training"
+    C ==> D
+    D ==> E
+end
+E ==> F
+```
+
+---
+
+## Usage
+
+### CLI
+
+The following command will train a sleep stage model using the reference configuration.
+
+```bash title="Terminal"
+sleepkit --task stage --mode train --config ./configuration.json
+```
+
+### Python
+
+The model can be trained using the following snippet:
+
+```py title="Python example" linenums="1"
+
+from pathlib import Path
+import sleepkit as sk
+
+task = sk.TaskFactory.get("detect")
+
+params = sk.TaskParams.model_validate_json(
+    Path("configuration.json").read_text()
+)
+
+task.train(params)
+```
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="modes-train-4.json" download="/sleepkit/examples/modes-train-4.json">
+
+```json title="modes-train-4.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+---
+
+## Arguments
+
+Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) for the list of arguments that can be used with the `train` command.
+
+---
+
+## Logging
+
+__sleepKIT__ provides built-in support for logging to several third-party services including [Weights & Biases](https://wandb.ai/site) (WANDB) and [TensorBoard](https://www.tensorflow.org/tensorboard).
+
+### WANDB
+
+The training mode is able to log all metrics and artifacts (aka models) to [Weights & Biases](https://wandb.ai/site) (WANDB). To enable WANDB logging, simply set environment variable `WANDB=1`. Remember to sign in prior to running experiments by running `wandb login`.
+
+### TensorBoard
+
+The training mode is able to log all metrics to [TensorBoard](https://www.tensorflow.org/tensorboard). To enable TensorBoard logging, simply set environment variable `TENSORBOARD=1`.
diff --git a/astro-site/src/content/docs/quickstart.mdx b/astro-site/src/content/docs/quickstart.mdx
new file mode 100644
index 0000000..9c2a11a
--- /dev/null
+++ b/astro-site/src/content/docs/quickstart.mdx
@@ -0,0 +1,277 @@
+---
+title: "Quickstart"
+description: "We provide several installation methods including pip, uv, and Docker. Install sleepKIT via pip/uv for the latest stable release or by cloning the GitHub repo…"
+---
+
+import ConfigExample from "../../components/ConfigExample.astro";
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Install sleepKIT
+
+We provide several installation methods including pip, uv, and Docker. Install __sleepKIT__ via pip/uv for the latest stable release or by cloning the GitHub repo for the most up-to-date. Additionally, a [VSCode Dev Container](https://code.visualstudio.com/docs/devcontainers/containers) is available and defined in [./.devcontainer](https://github.com/AmbiqAI/sleepkit/tree/main/.devcontainer) to run in an isolated Docker environment.
+
+<Tabs>
+<TabItem label="Git clone">
+
+Clone the repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package. In this mode, uv is recommended.
+
+```bash title="Terminal"
+# Clone the repository
+git clone https://github.com/AmbiqAI/sleepkit.git
+
+# Navigate to the cloned directory
+cd sleepkit
+
+# Install the package in editable mode for development
+uv sync
+```
+
+When using editable mode via uv, be sure to activate the python environment: `source .venv/bin/activate`. <br />
+On Windows using Powershell, use `.venv\Scripts\activate`.
+
+</TabItem>
+<TabItem label="PyPI install">
+
+Install the sleepKIT package using pip or uv.
+Visit the Python Package Index (PyPI) for more details on the package: [https://pypi.org/project/sleepkit/](https://pypi.org/project/sleepkit/)
+
+```bash title="Terminal"
+# Install with pip
+pip install sleepkit
+```
+
+Or, if you prefer to use uv, you can install the package with the following command:
+
+```bash title="Terminal"
+# Install with uv
+uv add sleepkit
+```
+
+Alternatively, you can install the latest development version directly from the GitHub repository. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @canary.
+
+```bash title="Terminal"
+pip install git+https://github.com/AmbiqAI/sleepkit.git@main
+```
+
+Or, using uv:
+
+```bash title="Terminal"
+uv add git+https://github.com/AmbiqAI/sleepkit.git@main
+```
+
+</TabItem>
+</Tabs>
+
+## Requirements
+
+* [Python 3.12–3.13](https://www.python.org)
+* [uv](https://docs.astral.sh/uv/getting-started/installation/)
+
+Check the project's [pyproject.toml](https://github.com/AmbiqAI/sleepkit/blob/main/pyproject.toml) file for a list of up-to-date Python dependencies. Note that the installation methods above install all required dependencies. The following are optional dependencies only needed when running `demo` command using Ambiq's evaluation board (`EVB`) backend:
+
+* [Arm GNU Toolchain ^12.2](https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads)
+* [Segger J-Link ^7.92](https://www.segger.com/downloads/jlink/)
+
+Once installed, __sleepKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced experimentation.
+
+---
+
+## Use sleepKIT with CLI
+
+The sleepKIT command line interface (CLI) allows for simple single-line commands without writing Python code. The CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `sleepkit` command. Check out the [CLI Guide](/sleepkit/usage/cli/) to learn more about available options.
+
+<Tabs>
+<TabItem label="Syntax">
+
+`sleepkit` commands use the following syntax:
+
+```bash title="Terminal"
+sleepkit --mode [MODE] --task [TASK] --config [CONFIG]
+```
+
+Or using short flags:
+
+```bash title="Terminal"
+sleepkit -m [MODE] -t [TASK] -c [CONFIG]
+```
+
+Where:
+
+* `MODE` is one of `download`, `feature`, `train`, `evaluate`, `export`, or `demo`
+* `TASK` is one of `detect`, `stage`, or `apnea`
+* `CONFIG` is configuration as JSON content or file path
+
+</TabItem>
+<TabItem label="Download">
+
+Download datasets specified in the configuration file.
+
+```bash title="Terminal"
+sleepkit -m download -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Feature">
+
+Extract features from the datasets using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -m feature -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Train">
+
+Train a sleep stage model using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -m train -t stage -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Evaluate">
+
+Evaluate the trained sleep stage model using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -m evaluate -t stage  -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Demo">
+
+Run demo on trained sleep stage model using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -m demo -t stage -c ./configuration.json
+```
+
+</TabItem>
+</Tabs>
+
+## Use sleepKIT with Python
+
+The __sleepKIT__ Python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for a variety of tasks. The package is designed to be simple and easy to use.
+
+For example, you can create a custom model, train it, evaluate its performance on a validation set, and even export a quantized TensorFlow Lite model for deployment. Check out the [Python Guide](/sleepkit/usage/python/) to learn more about using sleepKIT as a Python package.
+
+Load a task configuration, then call the steps you need. Replace `configuration.json` with a configuration for your dataset and model.
+
+```python title="run_task.py"
+from pathlib import Path
+import sleepkit as sk
+
+params = sk.TaskParams.model_validate_json(
+    Path("configuration.json").read_text()
+)
+task = sk.TaskFactory.get("stage")
+
+task.download(params)
+task.feature(params)
+task.train(params)
+task.evaluate(params)
+task.export(params)
+```
+
+The configuration controls dataset locations, features, class mapping and training settings. This example shows the configuration structure; adapt its paths and task-specific values before running it.
+
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="quickstart-14.json" download="/sleepkit/examples/quickstart-14.json">
+
+```json title="quickstart-14.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
diff --git a/docs/staging-baseline.md b/astro-site/src/content/docs/staging-baseline.mdx
similarity index 95%
rename from docs/staging-baseline.md
rename to astro-site/src/content/docs/staging-baseline.mdx
index 955cfe2..57e5f83 100644
--- a/docs/staging-baseline.md
+++ b/astro-site/src/content/docs/staging-baseline.mdx
@@ -1,12 +1,15 @@
-# Saved-feature sleep staging baseline
+---
+title: "Saved-feature sleep staging baseline"
+description: "sleepkit.recipes.staging prepares ordered, per-subject FS-W-PA-14 feature records for the historical SS-3-TCN-SM input contract. It provides ordinary Python…"
+---
 
 `sleepkit.recipes.staging` prepares ordered, per-subject FS-W-PA-14 feature records
 for the historical SS-3-TCN-SM input contract. It provides ordinary Python
 functions for reading, preprocessing, windowing, and checkpoint evaluation. This guide covers replay;
-[new-model training](staging-training.md) is a separate explicit flow. Neither
+[new-model training](/sleepkit/staging-training/) is a separate explicit flow. Neither
 path regenerates raw features.
 
-```python
+```python title="Python example"
 from sleepkit.recipes.staging import read_subject, prepare_subject, window_subject
 
 record = read_subject("/path/to/subject.h5")
@@ -122,7 +125,7 @@ there is no implicit filesystem split. Keep the cohort and source records with
 private evaluation evidence. Use the frozen project dependencies with the
 `detection` extra (Keras/TensorFlow and HDF5) for the first supported replay.
 
-```sh
+```sh title="Terminal"
 KERAS_BACKEND=tensorflow python -m sleepkit.recipes.staging.evaluation \
   --features-dir /path/to/fs-w-pa-14-60/mesa \
   --checkpoint /path/to/ss-3-tcn-sm/model.keras \
diff --git a/docs/staging-training.md b/astro-site/src/content/docs/staging-training.mdx
similarity index 95%
rename from docs/staging-training.md
rename to astro-site/src/content/docs/staging-training.mdx
index 8c46364..5a55544 100644
--- a/docs/staging-training.md
+++ b/astro-site/src/content/docs/staging-training.mdx
@@ -1,14 +1,17 @@
-# Train a reproducible saved-feature staging experiment
+---
+title: "Train a reproducible saved-feature staging experiment"
+description: "This additive recipe connects explicit subject membership, preprocessing, training, evaluation and a local model archive. It uses ordinary Python blocks and…"
+---
 
 This additive recipe connects explicit subject membership, preprocessing,
 training, evaluation and a local model archive. It uses ordinary Python blocks
 and Keras array adapters. It does not regenerate features from raw sensors.
-See [the saved-feature contract](staging-baseline.md) for feature order, label
+See [the saved-feature contract](/sleepkit/staging-baseline/) for feature order, label
 mapping, whole-subject normalization and its historical limitations.
 
 ## Start with explicit subjects
 
-```python
+```python title="Python example"
 from pathlib import Path
 from sleepkit.artifacts.package import write_json
 from sleepkit.recipes.staging.split import create_split
@@ -38,7 +41,7 @@ label-driven filtering, replacement sampling, or post-test resplitting is used.
 
 ## Experiment in Python
 
-```python
+```python title="Python example"
 from sleepkit.recipes.staging.data import prepare_partition
 from sleepkit.recipes.staging.recipe import Config, train, evaluate
 from sleepkit.recipes.staging.split import load_split
@@ -91,7 +94,7 @@ installation claim.
 Use the frozen TensorFlow environment for the first real golden experiment.
 Choose the configuration before model fitting or test evaluation:
 
-```sh
+```sh title="Terminal"
 KERAS_BACKEND=tensorflow python -m sleepkit.recipes.staging declare \
   --features-dir /private/fs-w-pa-14-60/mesa \
   --split /private/split.json \
diff --git a/astro-site/src/content/docs/tasks/apnea.mdx b/astro-site/src/content/docs/tasks/apnea.mdx
new file mode 100644
index 0000000..515649c
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/apnea.mdx
@@ -0,0 +1,65 @@
+---
+title: "Sleep Apnea Task"
+description: "Sleep apnea detection is the process of identifying hypopnea/apnea events. This task is useful for identifying sleep disorders and for monitoring sleep quality."
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Overview
+
+Sleep apnea detection is the process of identifying hypopnea/apnea events. This task is useful for identifying sleep disorders and for monitoring sleep quality.
+
+## Sleep Apnea Types
+
+There are three main types of sleep apnea: obstructive, central, and mixed. Obstructive sleep apnea (OSA) is the most common type of sleep apnea. It occurs when the throat muscles relax and block the airway during sleep. Central sleep apnea (CSA) occurs when the brain fails to send the proper signals to the muscles that control breathing. Mixed sleep apnea is a combination of both obstructive and central sleep apnea. Hypopnea is a partial blockage of the airway that results in shallow breathing.
+
+<Tabs>
+<TabItem label="Obstructive Sleep Apnea">
+
+Obstructive sleep apnea (OSA) is the most common type of sleep apnea. It occurs when the throat muscles relax and block the airway during sleep. This leads to pauses in breathing and can result in low oxygen levels in the blood. OSA is often associated with snoring and daytime sleepiness.
+
+</TabItem>
+<TabItem label="Central Sleep Apnea">
+
+Central sleep apnea (CSA) occurs when the brain fails to send the proper signals to the muscles that control breathing. This results in pauses in breathing during sleep. CSA is less common than OSA and is often associated with heart failure or stroke. Clinically, central sleep apnea is defined as a lack of respiratory effort for at least 10 seconds.
+
+</TabItem>
+<TabItem label="Mixed Sleep Apnea">
+
+Mixed sleep apnea is a combination of both obstructive and central sleep apnea. It occurs when there is a blockage of the airway and a failure of the brain to send the proper signals to the muscles that control breathing. Mixed sleep apnea is less common than OSA or CSA. Clinically, mixed sleep apnea is defined as a combination of obstructive and central apneas.
+
+</TabItem>
+<TabItem label="Hypopnea">
+
+Hypopnea is a partial blockage of the airway that results in shallow breathing. It is less severe than apnea but can still disrupt sleep and lead to daytime sleepiness. Clinically, hypopnea is defined as a reduction in airflow of at least 30% for at least 10 seconds, accompanied by a decrease in oxygen saturation of at least 3%.
+
+</TabItem>
+</Tabs>
+
+## Model Zoo
+
+The following table provides the latest performance and accuracy results for pre-trained models. Additional result details can be found in [Model Zoo → Apnea](/sleepkit/zoo/apnea/).
+
+| NAME             | LOCATION | # CLASSES | MODEL   | PARAMS | FLOPS   | ACCURACY | F1    |
+| ---------------- | -------- | --------- | ------- | ------ | ------- | -------- | ----- |
+| __SA-2-TCN-SM__  | Wrist    | 2         | TCN     | 5K     | 20M/hr  | 91.0%    | 91.0% |
+
+---
+
+## Target Classes
+
+Below outlines the classes available for apnea classification. When training a model, the number of classes, mapping, and names must be provided.
+
+| CLASS   | LABEL | DESCRIPTION |
+| ------- | ----- | ----------- |
+| NONE    | 0     | No apnea    |
+| HYPOPNEA| 1     | Shallow breathing |
+| CENTRAL | 2     | Central apnea |
+| OBSTRUCTIVE | 3 | Obstructive apnea |
+| MIXED   | 4     | Mixed apnea |
+| NOISE   | 5     | Noise |
+
+## References
+
+* [ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing](https://doi.org/10.48550/arXiv.2306.10863)
+* [Multimodal Sleep Apnea Detection with Missing or Noisy Modalities](https://arxiv.org/pdf/2402.17788v1.pdf)
diff --git a/astro-site/src/content/docs/tasks/arousal.mdx b/astro-site/src/content/docs/tasks/arousal.mdx
new file mode 100644
index 0000000..ea39ae2
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/arousal.mdx
@@ -0,0 +1,8 @@
+---
+title: "Sleep arousal"
+description: "Sleep arousal is not a registered sleepKIT task. This URL is retained for existing links; no arousal model or workflow is provided here."
+---
+
+Sleep arousal is not a registered sleepKIT task. This URL is retained for existing links; no arousal model or workflow is provided here.
+
+<a className="md-button" href="/sleepkit/tasks/">View supported tasks</a>
diff --git a/docs/tasks/byot.md b/astro-site/src/content/docs/tasks/byot.mdx
similarity index 79%
rename from docs/tasks/byot.md
rename to astro-site/src/content/docs/tasks/byot.mdx
index 986f7b5..f00d399 100644
--- a/docs/tasks/byot.md
+++ b/astro-site/src/content/docs/tasks/byot.mdx
@@ -1,13 +1,15 @@
-# Bring-Your-Own-Task (BYOT)
+---
+title: "Bring-Your-Own-Task (BYOT)"
+description: "The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for training, evaluating, and deploying sleep-related AI models. This feature is…"
+---
 
 The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for training, evaluating, and deploying sleep-related AI models. This feature is useful for creating custom workflows for a given application with minimal coding.
 
-
 ## How it Works
 
 1. **Create a Task**: Define a new task by creating a new Python file. The file should contain a class that inherits from the `sk.Task` base class and implements the required methods.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     import sleepkit as sk
 
     class CustomTask(sk.Task):
@@ -32,7 +34,7 @@ The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for t
 
 2. **Register the Task**: Register the new task with the `sk.TaskFactory` by calling the `register` method. This method takes the task name and the task class as arguments.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     ...
 
     sk.TaskFactory.register("custom", CustomTask)
@@ -40,7 +42,7 @@ The Bring-Your-Own-Task (BYOT) feature allows users to create custom tasks for t
 
 3. **Use the Task**: The new task can now be used with the `sk.TaskFactory` to perform various operations such as training, evaluating, and deploying models.
 
-    ```py linenums="1"
+    ```py title="Python example" linenums="1"
     ...
 
     params = sk.TaskParams(...)
diff --git a/astro-site/src/content/docs/tasks/detect.mdx b/astro-site/src/content/docs/tasks/detect.mdx
new file mode 100644
index 0000000..a1f3c42
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/detect.mdx
@@ -0,0 +1,57 @@
+---
+title: "Sleep Detection Task"
+description: "The objective of sleep detection is to identify periods of sustained sleep over the course of several days or weeks. The de facto standard for long-term,…"
+---
+
+## Overview
+
+The objective of sleep detection is to identify periods of sustained sleep over the course of several days or weeks. The de facto standard for long-term, ambulatory sleep detection is actigraphy, which is a method of monitoring gross motor activity using an accelerometer. However, actigraphy is not a reliable method for sleep detection as it employs very simple heuristics to determine sleep. Often actigraphy can misclassify periods of inactivity or even not-worn as detected sleep.
+
+In this task, we look to leverage a light-weight model that can outperform actigraphy similarly using only data from an IMU. For more advanced sleep analysis, refer to the [Sleep Stage Classification](/sleepkit/tasks/stage/). By leveraring Ambiq's ultra-low-power microcontroller along with an ultra-low-power IMU, an efficient AI enabled actigraphy or fitness band will be able to run for weeks off a single charge.
+
+<figure>
+  <img src="/sleepkit/assets/tasks/detect/ambiq-watch.webp" alt="Wrist-based Sleep Classification" width="540" />
+  <figcaption>Wrist-based Sleep Detection</figcaption>
+</figure>
+
+---
+
+## Model Zoo
+
+The following table provides the latest performance and accuracy results for pre-trained models. Additional result details can be found in [Model Zoo → Detect](/sleepkit/zoo/detect/).
+
+| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
+| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
+| __SD-2-TCN-SM__  | Wrist    | 2         | TCN   | 9K     | 1.5M/hr | 95.9%    | 94.5% |
+
+---
+
+## Target Classes
+
+Below outlines the classes available for sleep detect classification. When training a model, the number of classes, mapping, and names must be provided.
+
+| CLASS   | LABELS          |
+| ------- | --------------- |
+| 0       | AWAKE           |
+| 1       | SLEEP           |
+
+**Class Mapping**
+
+Below is an example of a class mapping for a 2-class sleep detect model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped.
+
+```json title="JSON example"
+{
+    "num_classes": 2,
+    "class_names": ["AWAKE", "SLEEP"],
+    "class_map": {
+        "0": 0,
+        "1": 1
+    }
+}
+```
+
+---
+
+## References
+
+* [AI-Driven sleep staging from actigraphy and heart rate](https://doi.org/10.1371/journal.pone.0285703)
diff --git a/docs/tasks/index.md b/astro-site/src/content/docs/tasks/index.mdx
similarity index 74%
rename from docs/tasks/index.md
rename to astro-site/src/content/docs/tasks/index.mdx
index 89cdd85..a7b323a 100644
--- a/docs/tasks/index.md
+++ b/astro-site/src/content/docs/tasks/index.mdx
@@ -1,34 +1,32 @@
-# :simple-task: Tasks
+---
+title: "Tasks"
+description: "sleepKIT provides several built-in sleep-monitoring related tasks. Each task is designed to address a unique aspect such as sleep staging and sleep apnea…"
+---
 
 ## Introduction
 
 sleepKIT provides several built-in __sleep-monitoring__ related tasks. Each task is designed to address a unique aspect such as sleep staging and sleep apnea detection. The tasks are designed to be modular and can be used independently or in combination to address specific use cases. In addition to the built-in tasks, custom tasks can be created by extending the `sk.Task` base class and registering it with the task factory.
 
 <figure markdown="span">
-  ![Task Diagram](../assets/tasks/sleepkit-task-diagram.svg){ width="600" }
+  <img src="/sleepkit/assets/tasks/sleepkit-task-diagram.svg" alt="Task Diagram" width="600" />
 </figure>
 
-
 ---
 
 ## Available Tasks
 
-###  [Detect](./detect.md)
+###  [Detect](/sleepkit/tasks/detect/)
 
 Sleep detection is the process of identifying sustained sleep/inactivity bouts. This task is useful for identifying long-term sleep patterns and for monitoring sleep quality.
 
-### [Stage](./stage.md)
+### [Stage](/sleepkit/tasks/stage/)
 
 Sleep stage classification is the process of identifying the different stages of sleep such as light, deep, and REM sleep. This task is useful for monitoring sleep quality and for identifying sleep disorders.
 
-### [Apnea](./apnea.md)
+### [Apnea](/sleepkit/tasks/apnea/)
 
 Sleep apnea detection is the process of identifying hypopnea/apnea events. This task is useful for identifying sleep disorders and for monitoring sleep quality.
 
-<!-- ### [Arousal](./arousal.md)
-
-Sleep arousal detection is the process of identifying sleep arousal events. This task is useful for identifying sleep disorders and for monitoring sleep quality. -->
-
-### [Bring-Your-Own-Task (BYOT)](./byot.md)
+### [Bring-Your-Own-Task (BYOT)](/sleepkit/tasks/byot/)
 
 Bring-Your-Own-Task (BYOT) is a feature that allows users to create custom tasks by extending the `sk.Task` base class and registering it with the task factory. This feature is useful for addressing specific use cases that are not covered by the built-in tasks.
diff --git a/astro-site/src/content/docs/tasks/stage.mdx b/astro-site/src/content/docs/tasks/stage.mdx
new file mode 100644
index 0000000..feb610f
--- /dev/null
+++ b/astro-site/src/content/docs/tasks/stage.mdx
@@ -0,0 +1,179 @@
+---
+title: "Sleep Stage Task"
+description: "For a saved-feature workflow, start with the staging baseline, which describes input records, preprocessing and checkpoint evaluation. Continue to staging…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Work with saved features
+
+For a saved-feature workflow, start with the [staging baseline](/sleepkit/staging-baseline/), which describes input records, preprocessing and checkpoint evaluation. Continue to [staging training](/sleepkit/staging-training/) to train a model with an explicit split and configuration.
+
+## Overview
+
+The objective of sleep stage assessment is to determine the different stages of sleep (N1, N2, N3, REM, and wake) over the course of a night. The de facto standard for sleep stage assessment is polysomnography (PSG) that requires collecting numerous multi-site physiological signals and a trained sleep technician to perform the assessment. The technician uses the physiological signals to determine the sleep stage based on the American Academy of Sleep Medicine (AASM) guidelines. The identification of stages is possible due to specific electrophysiological signatures present and recorded by EEG, EOG, and EMG signals.
+
+<figure>
+  <img src="/sleepkit/assets/tasks/stage/sleep-stage.svg" alt="Sleep Stage" width="640" />
+  <figcaption>Sleep Stage Assessment</figcaption>
+</figure>
+
+The focus of this project is to develop a model that can perform sleep stage assessment using only a small subset of the signals that can be captured from same body location (e.g. wrist) that is viable for long-term use. In particular, the current models are trained on physiological signals that can be captured from the wrist for fitness band and smartwatch applications. Traditionally, wrist-based sleep classification has been performed using actigraphy, which is a method of monitoring gross motor activity using an accelerometer. However, actigraphy is not a reliable method for sleep stage assessment. The current model is designed to perform sleep stage assessment using a combination of motor activity, cardiovascular signals (e.g. heart rate), and derived resipiratory signals (e.g. respiration rate).
+
+<figure>
+  <img src="/sleepkit/assets/tasks/stage/ambiq-watch.webp" alt="Wrist-based Sleep Classification" width="540" />
+  <figcaption>Wrist-based Sleep Classification</figcaption>
+</figure>
+
+---
+
+## Sleep Stages
+
+Sleep stages consists of wake (W), non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. NREM sleep is further divided into three stages: N1, N2, and N3. N1 and N2 are considered light sleep, while N3 is considered deep sleep. REM sleep is considered the dreaming stage of sleep. During the course of a night, a subject will undergo multiple rounds of sleep cycles, each composed of the individual stages. The duration of each cycle and length of each stage various throughout the night. Each sleep cycle typically lasts around 90 to 120 minutes.
+
+<figure class="sleep-stage-chart">
+<div class="sleep-stage-donut" role="img" aria-label="Sleep stage proportions: N1 5 percent, N2 55 percent, N3 10 percent, REM 30 percent."></div>
+<div class="sleep-stage-legend">
+
+| Stage | Share |
+| --- | ---: |
+| N1 | 5% |
+| N2 | 55% |
+| N3 | 10% |
+| REM | 30% |
+
+</div>
+<figcaption>Example sleep-stage proportions. The distribution varies between nights and individuals.</figcaption>
+</figure>
+
+<Tabs>
+<TabItem label="Wake (W)">
+
+This is the time before or after bed when individual is awake.
+
+Key Characteristics
+
+* Alpha waves on the EEG
+* No K-complexes or sleep spindles
+* Eye movements are normal
+* Respiratory rate and heart rate are normal
+* Body movements are common
+
+</TabItem>
+<TabItem label="Light Sleep (N1)">
+
+This is the shortest and lightest stage of sleep. It is the transition from wakefulness to sleep. It is characterized by slow eye movements, and alpha and theta waves on the EEG. It is also known as somnolence, or drowsy sleep. This stage lasts around 1 to 5 minutes, consisting of 5% of total sleep time. This stage is extremely difficult by EEG alone, and is often misclassified as wake or N2.
+
+Key Characteristics:
+
+* Alpha and theta waves on the EEG
+* No K-complexes or sleep spindles
+* Slow eye movements
+* Respiratory rate and heart rate are normal
+* Body temperature begins to drop
+* Muscles relax; might jerk
+
+</TabItem>
+<TabItem label="Core Sleep (N2)">
+
+This stage represents deeper sleep as the body begins to prepare for deep sleep. Physiologically, the body temperature begins to drop and the heart rate begins to slow. The stage is characterized by sleep spindles and K-complexes on the EEG. Stage 2 sleep lasts around 10 to 25 minutes, consisting of 45% to 55% of total sleep time. The duration increases as the night progresses.
+
+Key Characteristics:
+
+* Sleep spindles and K-complexes on the EEG
+* No eye movements
+* Respiratory rate and heart rate slows
+* Body temperature drops
+* Body movements are rare
+
+</TabItem>
+<TabItem label="Deep Sleep (N3)">
+
+Stage 3 sleep is the deepest stage of sleep. It is characterized by delta waves on the EEG. It is also known as slow-wave sleep (SWS) or delta sleep. It is difficult to wake someone from this stage. Stage 3 sleep lasts around 20 to 40 minutes, consisting of 3% to 8% of total sleep time. The duration decreases as the night progresses.
+
+Key Characteristics:
+
+* Delta waves on the EEG
+* No eye movements
+* Blood pressure drops, flow increases
+* Respiratory rate and heart rate are at lowest levels
+* Body movements are rare (sleep walking, sleep talking, bed wetting, and night terrors may occur)
+
+</TabItem>
+<TabItem label="REM Sleep (REM)">
+
+The REM stage is characterized by rapid eye movements, low muscle tone, and dreaming. It is also known as paradoxical sleep. It is difficult to wake someone from this stage. REM sleep lasts around 10 to 60 minutes, consisting of 20% to 25% of total sleep time. The duration increases as the night progresses.
+
+* Brain activity mimicks normal awake
+* Rapid eye movements
+* Respiration increases and irregular
+* Heart rate increases
+* Temperature regulation is off
+* Body becomes immobile
+
+</TabItem>
+</Tabs>
+
+---
+
+## Model Zoo
+
+The following table provides the latest performance and accuracy results for pre-trained models. Additional result details can be found in [Model Zoo → Stage](/sleepkit/zoo/stage/).
+
+| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
+| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
+| SS-2-TCN-SM      | Wrist    | 2         | TCN   | 10K    | 1.7M/hr | 88.9%    | 96.0% |
+| SS-3-TCN-SM      | Wrist    | 3         | TCN   | 11K    | 1.9M/hr | 84.4%    | 91.8% |
+| SS-4-TCN-SM      | Wrist    | 4         | TCN   | 26K    | 4.2M/hr | 75.5%    | 81.6% |
+| SS-5-TCN-SM      | Wrist    | 5         | TCN   | 28K    | 4.6M/hr | 68.4%    | 74.1% |
+
+---
+
+## Target Classes
+
+Below outlines the classes available for sleep stage classification. When training a model, the number of classes, mapping, and names must be provided.
+
+| CLASS   | LABELS          |
+| ------- | --------------- |
+| 0       | WAKE            |
+| 1       | STAGE1 (N1)     |
+| 2       | STAGE2 (N2)     |
+| 3       | STAGE3 (N3)     |
+| 4       | STAGE4 (N4)     |
+| 5       | REM             |
+| 6       | NOISE           |
+
+**Class Mapping**
+
+Below is an example of a class mapping for a 4-class sleep stage model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped.
+
+```json title="JSON example"
+{
+    "num_classes": 4,
+    "class_names": ["AWAKE", "LIGHT", "DEEP", "REM"],
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 2,
+        "4": 2,
+        "5": 3
+    }
+}
+```
+
+---
+
+## Limitations
+
+Inferring sleep stage without electrophysiological signals is an extremely challenging task. Especially when considering a highly noisy ambulatory environment. Furthermore, the limited time spent in certain sleep stages such as N1 makes it difficult to accurately classify these stages. This is highlighted by the limited interrater reliability of sleep stage scoring. A recent paper by [Kapur et al., 2021](https://doi.org/10.5664/jcsm.9538) performed a meta-analysis of 101 studies and found that the interrater reliability for stage N1 sleep was only fair, while the interrater reliabilities for stage N2 and N3 sleep were moderate. The paper found the Cohen’s kappa for manual, overall sleep scoring was 0.76, indicating substantial agreement (95% confidence interval, 0.71–0.81; P &lt; .001). By sleep stage, the figures were 0.70, 0.24, 0.57, 0.57, and 0.69 for the W, N1, N2, N3, and R stages, respectively. The interrater reliabilities for stage N2 and N3 sleep were moderate, and that for stage N1 sleep was only fair. Taking this into account, we should not expect to achieve perfect sleep stage classification.
+
+## References
+
+* [U-Sleep: Resilient High-Frequency Sleep Staging](https://doi.org/10.1038/s41746-021-00440-5)
+* [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://doi.org/10.48550/arXiv.1910.11162)
+* [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://doi.org/10.48550/arXiv.1703.04046)
+* [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://doi.org/10.48550/arXiv.2210.02186)
+* [The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring](https://doi.org/10.3390/s21134302)
+* [Interrater reliability of sleep stage scoring: a meta-analysis](https://doi.org/10.5664/jcsm.9538)
+* [Development of generalizable automatic sleep staging using heart rate and movement based on large databases](https://doi.org/10.1007/s13534-023-00288-6)
diff --git a/astro-site/src/content/docs/usage/cli.mdx b/astro-site/src/content/docs/usage/cli.mdx
new file mode 100644
index 0000000..40395d7
--- /dev/null
+++ b/astro-site/src/content/docs/usage/cli.mdx
@@ -0,0 +1,172 @@
+---
+title: "sleepKIT CLI"
+description: "The sleepKIT command line interface (CLI) makes it easy to run a variety of single-line commands without the need for writing any code. You can run all tasks…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+import AsciiTerminal from '@ambiqai/helia-ui/astro/AsciiTerminal';
+
+<AsciiTerminal
+  lines={[{"text":"sleepkit --help","kind":"command"},{"text":"","kind":"output"},{"text":"sleepKIT CLI Options:","kind":"output"},{"text":"    --task [detect, stage, apnea]","kind":"output"},{"text":"    --mode [download, feature, train, evaluate, export, demo]","kind":"output"},{"text":"    --config [\"./path/to/config.json\", or '{\"raw: \"json\"}']","kind":"output"}]}
+/>
+
+The sleepKIT command line interface (CLI) makes it easy to run a variety of single-line commands without the need for writing any code. You can run all tasks and modes from the terminal with the `sleepkit` command.
+
+<Tabs>
+<TabItem label="Syntax">
+
+`sleepkit` commands use the following syntax:
+
+```bash title="Terminal"
+sleepkit --mode [MODE] --task [TASK] --config [CONFIG]
+```
+
+Or using short flags:
+
+```bash title="Terminal"
+sleepkit -m [MODE] -t [TASK] -c [CONFIG]
+```
+
+Where:
+
+* `MODE` is one of `download`, `feature`, `train`, `evaluate`, `export`, or `demo`
+* `TASK` is one of `detect`, `stage`, or `apnea`
+* `CONFIG` is configuration as JSON content or file path
+
+</TabItem>
+<TabItem label="Download">
+
+Download datasets specified in the configuration file.
+
+```bash title="Terminal"
+sleepkit -t stage -m download -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Feature">
+
+Extract features from the datasets using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -t stage -m feature -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Train">
+
+Train a task model using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -t stage -m train -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Evaluate">
+
+Evaluate the trained task model using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -t stage -m evaluate  -c ./configuration.json
+```
+
+</TabItem>
+<TabItem label="Demo">
+
+Run demo on trained task model using the supplied configuration file.
+
+```bash title="Terminal"
+sleepkit -t stage -m demo -c ./configuration.json
+```
+
+</TabItem>
+</Tabs>
+
+:::note[Configuration File]
+The configuration file is a JSON file that contains all the necessary parameters for the task. The configuration file can be passed as a file path or as a JSON string. In addition, a single configuration file can be used for all `modes`- only needed parameters will be extracted for the given `mode` running.  Please refer to the [Configuration](/sleepkit/modes/configuration/) section for more details.
+:::
+
+---
+
+## [Download](/sleepkit/modes/download/)
+
+The `download` command is used to download all datasets specified in the configuration file. Please refer to [Datasets](/sleepkit/datasets/) for details on the available datasets.
+
+**CLI**
+
+The following command will download and prepare all datasets specified in configuration JSON file.
+
+```bash title="Terminal"
+sleepkit --task stage --mode download --config ./configuration.json
+```
+
+---
+
+## [Feature](/sleepkit/features/)
+
+The `feature` command is used to extract features from the datasets specified in the configuration file using the specified feature set generator. Additional parameters can be set in the configuration file. Please refer to [FeatureParams](/sleepkit/modes/configuration/#featureparams) and [TaskParams](/sleepkit/modes/configuration/#taskparams) to see supported options.
+
+**CLI**
+
+The following command will extract features from the datasets using the reference configuration:
+
+```bash title="Terminal"
+sleepkit --task stage --mode feature --config ./configuration.json
+```
+
+---
+
+## [Train](/sleepkit/modes/train/)
+
+The `train` command is used to train a sleepKIT model for the specified `task` and `feature set`. Each task provides a reference routine for training the model. The routine can be customized via the configuration file. Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) to see supported options.
+
+**CLI**
+
+The following command will train a task model using the reference configuration:
+
+```bash title="Terminal"
+sleepkit --task stage --mode train --config ./configuration.json
+```
+
+---
+
+## [Evaluate](/sleepkit/modes/evaluate/)
+
+The `evaluate` command will test the performance of the model on the reserved test sets for the specified `task`. The routine can be customized via the configuration file. Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) to see supported options.
+
+**CLI**
+
+The following command will test the task model using the reference configuration:
+
+```bash title="Terminal"
+sleepkit --task stage --mode evaluate --config ./configuration.json
+```
+
+---
+
+## [Export](/sleepkit/modes/export/)
+
+The `export` command will convert the trained TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file. Once converted, the TFLM header file will be copied to location specified by `tflm_file`. Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) to see supported options.
+
+**CLI**
+
+The following command will export the task model to TF Lite and TFLM:
+
+```bash title="Terminal"
+sleepkit --task stage --mode export --config ./configuration.json
+```
+
+---
+
+## [Demo](/sleepkit/modes/demo/)
+
+The `demo` command is used to run a task-level demonstration using either the PC or EVB as backend inference engine. Please refer to [TaskParams](/sleepkit/modes/configuration/#taskparams) to see supported options.
+
+**CLI**
+
+The following command will run a demo on the trained task model using the same supplied configuration file.
+
+```bash title="Terminal"
+sleepkit --task stage --mode demo --config ./configuration.json
+```
+
+---
diff --git a/astro-site/src/content/docs/usage/python.mdx b/astro-site/src/content/docs/usage/python.mdx
new file mode 100644
index 0000000..15b2255
--- /dev/null
+++ b/astro-site/src/content/docs/usage/python.mdx
@@ -0,0 +1,198 @@
+---
+title: "Python Usage"
+description: "sleepKIT python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for both built-in…"
+---
+
+import ConfigExample from "../../../components/ConfigExample.astro";
+
+__sleepKIT__ python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for both built-in tasks and custom tasks. In addition, custom datasets and model architectures can be created and registered with corresponding factories.
+
+## Overview
+
+The main components of sleepKIT include the following:
+
+### [Tasks](/sleepkit/tasks/)
+
+A [Task](/sleepkit/tasks/) inherits from the [sk.Task](/sleepkit/api/sleepkit/tasks/task) class and provides implementations for each of the main modes: download, feature, train, evaluate, export, and demo. Each mode is provided with a set of parameters defined by [sk.TaskParams](/sleepkit/api/sleepkit/defines). Additional task-specific parameters can be extended to the `TaskParams` class. These tasks are then registered and accessed via the `sk.TaskFactory` using a unique task name as the key and the custom Task class as the value.
+
+```py title="Python example" linenums="1"
+import sleepkit as sk
+
+task = sk.TaskFactory.get('stage')
+```
+
+### [Datasets](/sleepkit/datasets/)
+
+A dataset inherits from the [sk.Dataset](/sleepkit/api/sleepkit/datasets/dataset) class and provides implementations for downloading, preparing, and loading the dataset. Each dataset is provided with a set of custom parameters for initialization. The datasets are registered and accessed via the [DatasetFactory](/sleepkit/api/sleepkit/datasets/factory) using a unique dataset name as the key and the Dataset class as the value.
+
+```py title="Python example" linenums="1"
+import sleepkit as sk
+
+ds = sk.DatasetFactory.get('cmidss')(path='./datasets/cmidss')
+```
+
+### [Features](/sleepkit/features/)
+
+Since each task will require specific transformations of the data, a feature store is used to generate features from the dataset. The feature store provides a set of feature sets that can be used by the task. Each feature set is provided with a set of custom parameters for initialization. The feature sets are registered and accessed via the [sk.FeatureFactory](/sleepkit/api/sleepkit/features/factory) using a unique feature set name as the key and the Feature class as the value.
+
+### [Models](/sleepkit/models/)
+
+Lastly, sleepKIT leverages [helia-edge's](https://ambiqai.github.io/helia-edge/) customizable model architectures. To enable creating custom network topologies from configuration files, sleepKIT provides a `sk.ModelFactory` that allows you to create models by specifying the model key and the model parameters. Each item in the factory is a callable that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`.
+
+```python title="Python example"
+import keras
+import sleepkit as sk
+
+inputs = keras.Input((256, 1), dtype="float32")
+num_classes = 4
+model_params = {"blocks": [{"filters": 16, "kernel": [1, 3]}]}
+
+model = sk.ModelFactory.get('tcn')(
+    inputs=inputs,
+    params=model_params,
+    num_classes=num_classes
+)
+
+```
+
+## Usage
+
+### Running a built-in task w/ existing datasets
+
+1. Create a task configuration file defining the model, datasets, class labels, mode parameters, and so on. Have a look at the [sk.TaskParams](/sleepkit/modes/configuration/#taskparams) for more details on the available parameters.
+
+2. Leverage `sk.TaskFactory` to get the desired built-in task.
+
+3. Run the task's main modes: `download`, `feature`, `train`, `evaluate`, `export`, and/or `demo`.
+
+```py title="Python example" linenums="1"
+
+from pathlib import Path
+import sleepkit as sk
+
+params = sk.TaskParams.model_validate_json(
+    Path("configuration.json").read_text()
+)
+
+task = sk.TaskFactory.get("stage")
+
+task.download(params)  # Download dataset(s)
+
+task.feature(params)  # Generate features
+
+task.train(params)  # Train the model
+
+task.evaluate(params)  # Evaluate the model
+
+task.export(params)  # Export to TFLite
+
+```
+
+**Example configuration**
+<ConfigExample code={"{\n    \"name\": \"sd-2-tcn-sm\",\n    \"job_dir\": \"./results/sd-2-tcn-sm\",\n    \"verbose\": 2,\n\n    \"datasets\": [{\n        \"name\": \"cmidss\",\n        \"params\": {\n            \"path\": \"./datasets/cmidss\"\n        }\n    }],\n\n    \"feature\": {\n        \"name\": \"FS-W-A-5\",\n        \"sampling_rate\": 0.2,\n        \"frame_size\": 12,\n        \"loader\": \"hdf5\",\n        \"feat_key\": \"features\",\n        \"label_key\": \"detect_labels\",\n        \"mask_key\": \"mask\",\n        \"feat_cols\": null,\n        \"save_path\": \"./datasets/store/fs-w-a-5-60\",\n        \"params\": {}\n    },\n\n    \"sampling_rate\": 0.0083333,\n    \"frame_size\": 240,\n\n    \"num_classes\": 2,\n    \"class_map\": {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 1,\n        \"3\": 1,\n        \"4\": 1,\n        \"5\": 1\n    },\n    \"class_names\": [\"WAKE\", \"SLEEP\"],\n\n    \"samples_per_subject\": 100,\n    \"val_samples_per_subject\": 100,\n    \"test_samples_per_subject\": 50,\n\n    \"val_size\": 4000,\n    \"test_size\": 2500,\n\n    \"val_subjects\": 0.20,\n    \"batch_size\": 128,\n    \"buffer_size\": 10000,\n    \"epochs\": 200,\n    \"steps_per_epoch\": 25,\n    \"val_steps_per_epoch\": 25,\n    \"val_metric\": \"loss\",\n    \"lr_rate\": 1e-3,\n    \"lr_cycles\": 1,\n    \"label_smoothing\": 0,\n\n    \"test_metric\": \"f1\",\n    \"test_metric_threshold\": 0.02,\n    \"tflm_var_name\": \"sk_detect_flatbuffer\",\n    \"tflm_file\": \"sk_detect_flatbuffer.h\",\n\n    \"backend\": \"pc\",\n    \"display_report\": true,\n\n    \"quantization\": {\n        \"qat\": false,\n        \"mode\": \"INT8\",\n        \"io_type\": \"int8\",\n        \"concrete\": true,\n        \"debug\": false\n    },\n\n    \"model_file\": \"model.keras\",\n    \"use_logits\": false,\n    \"architecture\": {\n        \"name\": \"tcn\",\n        \"params\": {\n            \"input_kernel\": [1, 5],\n            \"input_norm\": \"batch\",\n            \"blocks\": [\n                {\"depth\": 1, \"branch\": 1, \"filters\": 16, \"kernel\": [1, 5], \"dilation\": [1, 1], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 32, \"kernel\": [1, 5], \"dilation\": [1, 2], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 48, \"kernel\": [1, 5], \"dilation\": [1, 4], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"},\n                {\"depth\": 1, \"branch\": 1, \"filters\": 64, \"kernel\": [1, 5], \"dilation\": [1, 8], \"dropout\": 0.10, \"ex_ratio\": 1, \"se_ratio\": 4, \"norm\": \"batch\"}\n            ],\n            \"output_kernel\": [1, 5],\n            \"include_top\": true,\n            \"use_logits\": true,\n            \"model_name\": \"tcn\"\n        }\n    }\n}"} lang="json" filename="usage-python-5.json" download="/sleepkit/examples/usage-python-5.json">
+
+```json title="usage-python-5.json"
+{
+    "name": "sd-2-tcn-sm",
+    "job_dir": "./results/sd-2-tcn-sm",
+    "verbose": 2,
+
+    "datasets": [{
+        "name": "cmidss",
+        "params": {
+            "path": "./datasets/cmidss"
+        }
+    }],
+
+    "feature": {
+        "name": "FS-W-A-5",
+        "sampling_rate": 0.2,
+        "frame_size": 12,
+        "loader": "hdf5",
+        "feat_key": "features",
+        "label_key": "detect_labels",
+        "mask_key": "mask",
+        "feat_cols": null,
+        "save_path": "./datasets/store/fs-w-a-5-60",
+        "params": {}
+    },
+
+    "sampling_rate": 0.0083333,
+    "frame_size": 240,
+
+    "num_classes": 2,
+    "class_map": {
+        "0": 0,
+        "1": 1,
+        "2": 1,
+        "3": 1,
+        "4": 1,
+        "5": 1
+    },
+    "class_names": ["WAKE", "SLEEP"],
+
+    "samples_per_subject": 100,
+    "val_samples_per_subject": 100,
+    "test_samples_per_subject": 50,
+
+    "val_size": 4000,
+    "test_size": 2500,
+
+    "val_subjects": 0.20,
+    "batch_size": 128,
+    "buffer_size": 10000,
+    "epochs": 200,
+    "steps_per_epoch": 25,
+    "val_steps_per_epoch": 25,
+    "val_metric": "loss",
+    "lr_rate": 1e-3,
+    "lr_cycles": 1,
+    "label_smoothing": 0,
+
+    "test_metric": "f1",
+    "test_metric_threshold": 0.02,
+    "tflm_var_name": "sk_detect_flatbuffer",
+    "tflm_file": "sk_detect_flatbuffer.h",
+
+    "backend": "pc",
+    "display_report": true,
+
+    "quantization": {
+        "qat": false,
+        "mode": "INT8",
+        "io_type": "int8",
+        "concrete": true,
+        "debug": false
+    },
+
+    "model_file": "model.keras",
+    "use_logits": false,
+    "architecture": {
+        "name": "tcn",
+        "params": {
+            "input_kernel": [1, 5],
+            "input_norm": "batch",
+            "blocks": [
+                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
+                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
+            ],
+            "output_kernel": [1, 5],
+            "include_top": true,
+            "use_logits": true,
+            "model_name": "tcn"
+        }
+    }
+}
+```
+
+</ConfigExample>
+
+### Running a custom task w/ custom datasets
+
+To create a custom task, check out the [Bring-Your-Own-Task Guide](/sleepkit/tasks/byot/).
+
+To create a custom dataset, check out the [Bring-Your-Own-Dataset Guide](/sleepkit/datasets/byod/).
+
+---
diff --git a/astro-site/src/content/docs/zoo/apnea.mdx b/astro-site/src/content/docs/zoo/apnea.mdx
new file mode 100644
index 0000000..b45e277
--- /dev/null
+++ b/astro-site/src/content/docs/zoo/apnea.mdx
@@ -0,0 +1,90 @@
+---
+title: "Sleep Apnea Detection"
+description: "The following table provides the latest pre-trained models for sleep apnea. Below we also provide additional details including training configuration, accuracy…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Model Overview
+
+The following table provides the latest pre-trained models for sleep apnea. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
+
+| NAME             | LOCATION | # CLASSES | MODEL   | PARAMS | FLOPS   | ACCURACY | F1    |
+| ---------------- | -------- | --------- | ------- | ------ | ------- | -------- | ----- |
+| __SA-2-TCN-SM__  | Wrist    | 2         | TCN     | 5K     | 20M/hr  | 91.0%    | 91.0% |
+
+## Model Details
+
+<Tabs>
+<TabItem label="SA-2-TCN-SM">
+
+The __SA-2-TCN-SM__ model is a 2-stage sleep apnea detection model that uses a Temporal convolutional network (TCN). The model is trained on PPG sensor data collected from the wrist and is able to locate apnea/hypopnea events.
+
+- **Location**: Wrist
+- **Classes**: Normal, Apnea
+- **Frame Size**: 10 minutes
+- **Datasets**: [MESA](/sleepkit/datasets/mesa/)
+- **Features**: [FS-W-P-5](/sleepkit/features/fs_w_p_5/)
+
+</TabItem>
+</Tabs>
+
+## Model Performance
+
+<Tabs>
+<TabItem label="SA-2-TCN-SM">
+
+The following plots show the model's performance in detecting apnea/hypopnea events. The first plot shows the confusion matrix for apnea detection.
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/apnea/sa-2-tcn-sm-cm.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+The following plot shows the model's ability to detect AHI (Apnea-Hypopnea Index) compared to the ground truth AHI values. The x-axis represents the true AHI values, while the y-axis represents the predicted AHI values. The plot shows a strong correlation between the true and predicted AHI values.
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/apnea/sa-2-tcn-sm-ahi-scatter.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+The following table provides the corresponding confusion matrix for AHI.
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/apnea/sa-2-tcn-sm-ahi-cm.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+</TabItem>
+</Tabs>
+
+---
+
+## EVB Performance
+
+The following table provides the latest performance results when running on Apollo4 Plus EVB. These results are obtained using neuralSPOTs [Autodeploy tool](https://ambiqai.github.io/neuralSPOT/docs/From%20TF%20to%20EVB%20-%20testing%2C%20profiling%2C%20and%20deploying%20AI%20models.html). From neuralSPOT repo, the following command can be used to capture EVB results via Autodeploy:
+
+```console title="Terminal"
+python -m ns_autodeploy \
+--tflite-filename model.tflite \
+--model-name model \
+--cpu-mode 192 \
+--arena-size-scratch-buffer-padding 0 \
+--max-arena-size 60 \
+```
+
+| Name            | Params   | FLOPS   | Metric     | Time     | Arena | Energy     |
+| --------------- | -------- | ------- | ---------- | -------- | ----- | ---------- |
+| __SA-2-TCN-SM__ | 10K      | 44M/hr  | 91.0% F1   | 1.8s/hr  |  61KB |  17.0mJ/hr |
+
+## Downloads
+
+<Tabs>
+<TabItem label="SA-2-TCN-SM">
+
+| Asset                                                                | Description                   |
+| -------------------------------------------------------------------- | ----------------------------- |
+| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/configuration.json)   | Configuration file            |
+| [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/model.keras)            | Keras Model file              |
+| [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/model.tflite)       | TFLite Model file             |
+| [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/metrics.json)       | Metrics file                  |
+
+</TabItem>
+</Tabs>
diff --git a/astro-site/src/content/docs/zoo/detect.mdx b/astro-site/src/content/docs/zoo/detect.mdx
new file mode 100644
index 0000000..b906f3a
--- /dev/null
+++ b/astro-site/src/content/docs/zoo/detect.mdx
@@ -0,0 +1,98 @@
+---
+title: "Sleep Detection Model"
+description: "The following table provides the latest pre-trained models for sleep detection. Below we also provide additional details including training configuration,…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Model Overview
+
+The following table provides the latest pre-trained models for sleep detection. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
+
+| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
+| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
+| __SD-2-TCN-SM__  | Wrist    | 2         | TCN   | 9K     | 1.5M/hr | 95.9%    | 94.5% |
+
+---
+
+## Model Details
+
+<Tabs>
+<TabItem label="SD-2-TCN-SM">
+
+The __SD-2-TCN-SM__ model is a 2-stage sleep detection model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on accelerometer sensor data collected from the wrist and is able to classify sleep and wake stages.
+
+- **Location**: Wrist
+- **Classes**: Awake, Sleep
+- **Frame Size**: 2 hours
+- **Datasets**: [MESA](/sleepkit/datasets/mesa/)
+- **Features**: [FS-W-A-5](/sleepkit/features/fs_w_a_5/)
+
+</TabItem>
+</Tabs>
+
+## Model Performance
+
+<Tabs>
+<TabItem label="SD-2-TCN-SM">
+
+### Confusion Matrix
+
+The confusion matrix provides a detailed breakdown of the model's performance on the test set. The rows represent the true labels, while the columns represent the predicted labels. The diagonal elements represent the number of correct predictions for each class, while the off-diagonal elements represent the number of incorrect predictions.
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/detect/detect-2-cm.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+### Sleep Efficiency Plot
+
+The following plot shows the model's performance in detecting sleep efficieny- percentage of time spent asleep while in bed. The x-axis represents the true sleep efficiency, while the y-axis represents the predicted sleep efficiency.
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/detect/detect-2-eff.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+### Total Sleep Time (TST) Plot
+
+The following plot shows the model's performance in detecting total sleep time (TST). The x-axis represents the true TST, while the y-axis represents the predicted TST. The plot shows a strong correlation between the true and predicted TST values.
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/detect/detect-2-tst.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+</TabItem>
+</Tabs>
+
+---
+
+## EVB Performance
+
+The following table provides the latest performance results when running on Apollo4 Plus EVB. These results are obtained using neuralSPOTs [Autodeploy tool](https://ambiqai.github.io/neuralSPOT/docs/From%20TF%20to%20EVB%20-%20testing%2C%20profiling%2C%20and%20deploying%20AI%20models.html). From neuralSPOT repo, the following command can be used to capture EVB results via Autodeploy:
+
+```console title="Terminal"
+python -m ns_autodeploy \
+--tflite-filename model.tflite \
+--model-name model \
+--cpu-mode 192 \
+--arena-size-scratch-buffer-padding 0 \
+--max-arena-size 60 \
+```
+
+| Name            | Params   | FLOPS   | Metric     | Time    | Arena | Energy     |
+| --------------- | -------- | ------- | ---------- | ------- | ----- | ---------- |
+| __SD-2-TCN-SM__ | 9K       | 1.5M/hr | 94.5% AP   | 61ms/hr |  34KB | 586uJ/hr   |
+
+## Downloads
+
+<Tabs>
+<TabItem label="SD-2-TCN-SM">
+
+| Asset                                                                | Description                   |
+| -------------------------------------------------------------------- | ----------------------------- |
+| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/configuration.json)   | Configuration file            |
+| [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/model.keras)            | Keras Model file              |
+| [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/model.tflite)       | TFLite Model file             |
+| [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/metrics.json)       | Metrics file                  |
+
+</TabItem>
+</Tabs>
diff --git a/astro-site/src/content/docs/zoo/index.mdx b/astro-site/src/content/docs/zoo/index.mdx
new file mode 100644
index 0000000..cd353a5
--- /dev/null
+++ b/astro-site/src/content/docs/zoo/index.mdx
@@ -0,0 +1,38 @@
+---
+title: "Model Zoo"
+description: "A number of pre-trained models are available for download to use in your own project. These models are trained on the datasets listed below and are available…"
+---
+
+A number of pre-trained models are available for download to use in your own project. These models are trained on the datasets listed below and are available in TensorFlow flatbuffer formats.
+
+Model licenses are specific to each release and its data sources. Public downloads
+do not imply commercial-use permission or a BSD license for weights. See the
+[licensing policy](/sleepkit/model-licensing-policy/); historical models have not yet
+received a complete per-artifact provenance and licensing review.
+
+## [Sleep Detect](/sleepkit/zoo/detect/)
+
+The following table provides the latest performance and accuracy results for sleep detection models.
+
+| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
+| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
+| __SD-2-TCN-SM__  | Wrist    | 2         | TCN   | 9K     | 1.5M/hr | 95.9%    | 94.5% |
+
+## [Sleep Stage](/sleepkit/zoo/stage/)
+
+The following table provides the latest performance and accuracy results for sleep staging models.
+
+| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
+| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
+| SS-2-TCN-SM      | Wrist    | 2         | TCN   | 10K    | 1.7M/hr | 88.9%    | 96.0% |
+| SS-3-TCN-SM      | Wrist    | 3         | TCN   | 11K    | 1.9M/hr | 84.4%    | 91.8% |
+| SS-4-TCN-SM      | Wrist    | 4         | TCN   | 26K    | 4.2M/hr | 75.5%    | 81.6% |
+| SS-5-TCN-SM      | Wrist    | 5         | TCN   | 28K    | 4.6M/hr | 68.4%    | 74.1% |
+
+## [Sleep Apnea](/sleepkit/zoo/apnea/)
+
+The following table provides the latest performance and accuracy results for sleep apnea detection models.
+
+| NAME             | LOCATION | # CLASSES | MODEL   | PARAMS | FLOPS   | ACCURACY | F1    |
+| ---------------- | -------- | --------- | ------- | ------ | ------- | -------- | ----- |
+| __SA-2-TCN-SM__  | Wrist    | 2         | TCN     | 5K     | 20M/hr  | 91.0%    | 91.0% |
diff --git a/astro-site/src/content/docs/zoo/stage.mdx b/astro-site/src/content/docs/zoo/stage.mdx
new file mode 100644
index 0000000..29ce59f
--- /dev/null
+++ b/astro-site/src/content/docs/zoo/stage.mdx
@@ -0,0 +1,245 @@
+---
+title: "Sleep Stage Models"
+description: "The following table provides the latest pre-trained models for sleep detection. Below we also provide additional details including training configuration,…"
+---
+
+import { Tabs, TabItem } from '@astrojs/starlight/components';
+
+## Model Overview
+
+The following table provides the latest pre-trained models for sleep detection. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
+
+| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
+| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
+| SS-2-TCN-SM      | Wrist    | 2         | TCN   | 10K    | 1.7M/hr | 88.9%    | 96.0% |
+| SS-3-TCN-SM      | Wrist    | 3         | TCN   | 11K    | 1.9M/hr | 84.4%    | 91.8% |
+| SS-4-TCN-SM      | Wrist    | 4         | TCN   | 26K    | 4.2M/hr | 75.5%    | 81.6% |
+| SS-5-TCN-SM      | Wrist    | 5         | TCN   | 28K    | 4.6M/hr | 68.4%    | 74.1% |
+
+## Model Details
+
+<Tabs>
+<TabItem label="SS-2-TCN-SM">
+
+The __SS-2-TCN-SM__ model is a 2-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify sleep and wake stages. The model requires only 10K parameters and achieves an accuracy of 88.8% and an average AP score of 96.2%.
+
+- **Location**: Wrist
+- **Classes**: Awake, Sleep
+- **Frame Size**: 2 hours
+- **Datasets**: [MESA](/sleepkit/datasets/mesa/)
+- **Features**: [FS-W-PA-14](/sleepkit/features/fs_w_pa_14/)
+
+| Base Class              | Target Class | Label     |
+| ----------------------- | ------------ | --------- |
+| 0-WAKE                  | 0            | WAKE      |
+| 1-N1, 2-N2, 3-N3, 5-REM | 1            | SLEEP     |
+
+</TabItem>
+<TabItem label="SS-3-TCN-SM">
+
+The __SS-3-TCN-SM__ model is a 3-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify awake, sleep and rem stages. The model requires only 14K parameters and achieves an accuracy of 84% and an average AP score of 91.5%.
+
+- **Location**: Wrist
+- **Classes**: Awake, NREM, REM
+- **Frame Size**: 2 hours
+- **Datasets**: [MESA](/sleepkit/datasets/mesa/)
+- **Features**: [FS-W-PA-14](/sleepkit/features/fs_w_pa_14/)
+
+| Base Class              | Target Class | Label     |
+| ----------------------- | ------------ | --------- |
+| 0-WAKE                  | 0            | WAKE      |
+| 1-N1, 2-N2, 3-N3        | 1            | NREM      |
+| 5-REM                   | 2            | REM       |
+
+</TabItem>
+<TabItem label="SS-4-TCN-SM">
+
+The __SS-4-TCN-SM__ model is a 4-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify awake, core, deep, and rem stages. The model requires only 18K parameters and achieves an accuracy of 75.8% and an average AP score of 83.1%.
+
+- **Location**: Wrist
+- **Classes**: Awake, Core, Deep, REM
+- **Frame Size**: 2 hours
+- **Datasets**: [MESA](/sleepkit/datasets/mesa/)
+- **Features**: [FS-W-PA-14](/sleepkit/features/fs_w_pa_14/)
+
+| Base Class              | Target Class | Label     |
+| ----------------------- | ------------ | --------- |
+| 0-WAKE                  | 0            | WAKE      |
+| 1-N1, 2-N2              | 1            | CORE      |
+| 3-N3                    | 2            | DEEP      |
+| 5-REM                   | 3            | REM       |
+
+</TabItem>
+<TabItem label="SS-5-TCN-SM">
+
+The __SS-5-TCN-SM__ model is a 5-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify awake, n1, n2, n3, and rem stages. The model requires only 17K parameters and achieves an accuracy of 70.4% and an average AP score of 76.4%.
+
+- **Location**: Wrist
+- **Classes**: Awake, N1, N2, N3, REM
+- **Frame Size**: 2 hours
+- **Datasets**: [MESA](/sleepkit/datasets/mesa/)
+- **Features**: [FS-W-PA-14](/sleepkit/features/fs_w_pa_14/)
+
+| Base Class              | Target Class | Label     |
+| ----------------------- | ------------ | --------- |
+| 0-WAKE                  | 0            | WAKE      |
+| 1-N1                    | 1            | N1        |
+| 2-N2                    | 2            | N2        |
+| 3-N3                    | 3            | N3        |
+| 5-REM                   | 4            | REM       |
+
+</TabItem>
+</Tabs>
+
+---
+
+## Model Performance
+
+<Tabs>
+<TabItem label="SS-2-TCN-SM">
+
+### Confusion Matrix
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/ss-2-tcn-sm-cm.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+### Sleep Efficiency Plot
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/ss-2-tcn-sm-eff.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+### Total Sleep Time (TST) Plot
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/ss-2-tcn-sm-tst.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+</TabItem>
+<TabItem label="SS-3-TCN-SM">
+
+### Confusion Matrix
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/ss-3-tcn-sm-cm.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+</TabItem>
+<TabItem label="SS-4-TCN-SM">
+
+### Confusion Matrix
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/ss-4-tcn-sm-cm.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+</TabItem>
+<TabItem label="SS-5-TCN-SM">
+
+### Confusion Matrix
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/ss-5-tcn-sm-cm.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+</TabItem>
+</Tabs>
+
+---
+
+## EVB Performance
+
+The following table provides the latest performance and accuracy results of all models when running on Apollo4 Plus EVB. These results are obtained using neuralSPOTs [Autodeploy tool](https://ambiqai.github.io/neuralSPOT/docs/From%20TF%20to%20EVB%20-%20testing%2C%20profiling%2C%20and%20deploying%20AI%20models.html). From neuralSPOT repo, the following command can be used to capture EVB results via Autodeploy:
+
+``` console
+python -m ns_autodeploy \
+--tflite-filename model.tflite \
+--model-name model \
+--cpu-mode 192 \
+--arena-size-scratch-buffer-padding 0 \
+--max-arena-size 80 \
+
+```
+
+| Name            | Params   | FLOPS   | Metric     | Time     | Arena | Energy    |
+| --------------- | -------- | ------- | ---------- | -------- | ----- | --------- |
+| SD-2-TCN-SM     | 10K      | 1.7M/hr | 96.0% AP   | 66ms/hr  | 32KB  |  705uJ/hr |
+| SD-3-TCN-SM     | 11K      | 1.9M/hr | 91.8% AP   | 75ms/hr  | 34KB  |  710uJ/hr |
+| SD-4-TCN-SM     | 26K      | 4.2M/hr | 81.6% AP   | 129ms/hr | 47KB  | 1.23mJ/hr |
+| SD-5-TCN-SM     | 28K      | 4.6M/hr | 74.1% AP   | 172ms/hr | 50KB  | 1.65mJ/hr |
+
+In addition, we can capture statistics from each layer. The following bar plot provides the latency of each block in the 4-stage sleep classification TCN model. For example, `ENC` refers to initial encoder 1-d seperable convulional layer, `B1.1` refers to all the layers in block 1, depth 1, `B1.2` refers to block 1, depth 2, and so on. We can see that as we go deeper into the network we see an increase in latency due to the increasing number of channels. The final `DEC` layer refers to the decoder layer which is a 1-d convolutional layer with 3 output channels (4 classes).
+
+<div class="sk-plotly-graph-div">
+<iframe title="Interactive example" src="/sleepkit/assets/zoo/stage/block-latency.html" loading="lazy" style={{width:'100%',height:'480px',border:0}} />
+</div>
+
+---
+
+## Comparison
+
+We compare our 3-stage and 4-stage model to the SLAMSS model from [Song et al., 2023](https://doi.org/10.1371/journal.pone.0285703). Their model was also trained on MESA dataset using only motor and cardiac physiological signals. In particular, they extract activity count, heart rate, and heart rate standard deviation in 30 second epochs. They fed 12 epochs (6 minutes) of the 3 features (12x6) as input to the network. The newtork consists of 3 1-D CNN layers, 2 LSTM layers, and 1 attention layer. The underlying design of the attention block is unclear but using only the 3 CNN and 2 LSTM layers the network requires roughly 8.8 MFLOPS per epoch. This equates to roughly __450X__ more computation (__1,056 MFLOPS/hr__) compared to our 4-stage sleep classification model (__2.3 MFLOPS/hr__).
+
+### 3-Stage Sleep Classification (MESA)
+
+| Reference         | Acc       | F1        | WAKE      | NREM      | REM       |
+| ----------------- | --------- | --------- | --------- | --------- | --------- |
+| [Song et al., 2023](https://doi.org/10.1371/journal.pone.0285703) | 79.1      | 80.0      | 78.0      | 81.8      | 70.9      |
+| **sleepKIT**      | **83.9**  | **84.2**  | **80.3**  | **86.6**  | **83.5**  |
+
+### 4-Stage Sleep Classification (MESA)
+
+| Reference         | Acc       | F1        | WAKE      | CORE      | DEEP      | REM       |
+| ----------------- | --------- | --------- | --------- | --------- | --------- | --------- |
+| [Song et al., 2023](https://doi.org/10.1371/journal.pone.0285703) | 70.0      | 72.0      | 78.7      | 66.3      | **55.9**      | 63.0      |
+| sleepKIT          | **75.8**  | **76.4**  | **80.6**  | **73.9**  | 52.2  | **81.7**  |
+
+---
+
+## Downloads
+
+<Tabs>
+<TabItem label="SS-2-TCN-SM">
+
+| Asset                                                                | Description                   |
+| -------------------------------------------------------------------- | ----------------------------- |
+| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/configuration.json)   | Configuration file            |
+| [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/model.keras)            | Keras Model file              |
+| [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/model.tflite)       | TFLite Model file             |
+| [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/metrics.json)       | Metrics file                  |
+
+</TabItem>
+<TabItem label="SS-3-TCN-SM">
+
+| Asset                                                                | Description                   |
+| -------------------------------------------------------------------- | ----------------------------- |
+| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/configuration.json)   | Configuration file            |
+| [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/model.keras)            | Keras Model file              |
+| [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/model.tflite)       | TFLite Model file             |
+| [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/metrics.json)       | Metrics file                  |
+
+</TabItem>
+<TabItem label="SS-4-TCN-SM">
+
+| Asset                                                                | Description                   |
+| -------------------------------------------------------------------- | ----------------------------- |
+| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/configuration.json)   | Configuration file            |
+| [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/model.keras)            | Keras Model file              |
+| [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/model.tflite)       | TFLite Model file             |
+| [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/metrics.json)       | Metrics file                  |
+
+</TabItem>
+<TabItem label="SS-5-TCN-SM">
+
+| Asset                                                                | Description                   |
+| -------------------------------------------------------------------- | ----------------------------- |
+| [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/configuration.json)   | Configuration file            |
+| [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/model.keras)            | Keras Model file              |
+| [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/model.tflite)       | TFLite Model file             |
+| [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/metrics.json)       | Metrics file                  |
+
+</TabItem>
+</Tabs>
+
+---
diff --git a/astro-site/src/navigation.mjs b/astro-site/src/navigation.mjs
index 6be7774..9dae75a 100644
--- a/astro-site/src/navigation.mjs
+++ b/astro-site/src/navigation.mjs
@@ -1,14 +1,6 @@
-import sidebar from "./data/sidebar.json" with { type: "json" };
 import apiSidebar from "./data/api-sidebar.json" with { type: "json" };
 const page = (label, slug) => ({ label, slug });
 const group = (label, items) => ({ label, items, collapsed: false });
-const original = (label) =>
-  sidebar
-    .find((item) => item.label === label)
-    .items.map((item) => ({
-      ...item,
-      slug: item.slug?.replace(/\.ipynb$/, ""),
-    }));
 const apiGroups = Object.entries(
   Object.groupBy(apiSidebar, (item) => item.label.split(".")[1] || "Package"),
 ).map(([label, items]) => ({ label, items, collapsed: true }));
@@ -37,9 +29,76 @@ export const sections = [
         page("Export", "modes/export"),
         page("Demo", "modes/demo"),
       ]),
-      group("Datasets", original("Datasets")),
-      group("Feature extraction", original("Features")),
-      group("Model architectures", original("Models")),
+      group("Datasets", [
+  {
+    "label": "Datasets",
+    "slug": "datasets"
+  },
+  {
+    "label": "CMIDSS",
+    "slug": "datasets/cmidss"
+  },
+  {
+    "label": "MESA",
+    "slug": "datasets/mesa"
+  },
+  {
+    "label": "YSYW",
+    "slug": "datasets/ysyw"
+  },
+  {
+    "label": "STAGES",
+    "slug": "datasets/stages"
+  },
+  {
+    "label": "Synthetic",
+    "slug": "datasets/synthetic"
+  },
+  {
+    "label": "BYOD",
+    "slug": "datasets/byod"
+  }
+]),
+      group("Feature extraction", [
+  {
+    "label": "Features",
+    "slug": "features"
+  },
+  {
+    "label": "FS-W-PA-14",
+    "slug": "features/fs_w_pa_14"
+  },
+  {
+    "label": "FS-C-EAR-9",
+    "slug": "features/fs_c_ear_9"
+  },
+  {
+    "label": "FS-W-A-5",
+    "slug": "features/fs_w_a_5"
+  },
+  {
+    "label": "FS-H-E-10",
+    "slug": "features/fs_h_e_10"
+  },
+  {
+    "label": "FS-W-P-5",
+    "slug": "features/fs_w_p_5"
+  },
+  {
+    "label": "BYOFS",
+    "slug": "features/byofs"
+  }
+]),
+      group("Model architectures", [
+  {
+    "label": "Models",
+    "slug": "models"
+  },
+  {
+    "label": "BYOM",
+    "slug": "models/byom"
+  }
+]),
       group("Tutorials", [
         page("Train a detection model", "guides/train-detect-model"),
         page("Staging ablation", "guides/stage-ablation"),
@@ -71,7 +130,24 @@ export const sections = [
     sidebar: [
       page("Python API catalog", "reference"),
       group("Model zoo", [
-        ...original("Model Zoo"),
+        ...[
+  {
+    "label": "Model Zoo",
+    "slug": "zoo"
+  },
+  {
+    "label": "Detect",
+    "slug": "zoo/detect"
+  },
+  {
+    "label": "Stage",
+    "slug": "zoo/stage"
+  },
+  {
+    "label": "Apnea",
+    "slug": "zoo/apnea"
+  }
+],
         page("Model artifacts", "huggingface-artifacts"),
       ]),
       group("Licensing", [
diff --git a/astro-site/src/redirects.json b/astro-site/src/redirects.json
new file mode 100644
index 0000000..0967ef4
--- /dev/null
+++ b/astro-site/src/redirects.json
@@ -0,0 +1 @@
+{}
diff --git a/docs/detection-preparation-performance.md b/docs-maintainers/detection-preparation-performance.md
similarity index 93%
rename from docs/detection-preparation-performance.md
rename to docs-maintainers/detection-preparation-performance.md
index ae6c4fa..bbb487b 100644
--- a/docs/detection-preparation-performance.md
+++ b/docs-maintainers/detection-preparation-performance.md
@@ -2,7 +2,7 @@
 
 This maintainer note documents an optimization to sleep-detection feature extraction and the checks used to preserve its outputs. It is useful when changing the extractor or reproducing the performance comparison; no extra setup is needed to use it.
 
-For a training workflow, start with the [sleep detection task](tasks/detect.md) or the [golden experiment](detection-golden.md).
+For a training workflow, start with the [sleep detection task](../astro-site/src/content/docs/tasks/detect.mdx) or the [golden experiment](../astro-site/src/content/docs/detection-golden.mdx).
 
 ## What changed
 
@@ -58,7 +58,7 @@ not a controlled cross-backend or hardware performance certification.
 
 ## Recorded equivalence and extraction timing
 
-The [full training comparison](evidence/membership-vectorized-equivalence-20260920/report.json)
+The [full training comparison](../astro-site/public/evidence/membership-vectorized-equivalence-20260920/report.json)
 passed for all **14,857,307 feature frames from 193 training series**. Feature
 values, validity masks and endpoints matched byte for byte. The fitted normalizer
 fingerprint also equals the original CPU profile's fingerprint. Measurements used
@@ -82,7 +82,7 @@ no recordings, features or subject identifiers are included.
 
 ## Full recipe stage profile
 
-A [fresh-cache profile of the revised recipe](evidence/membership-vectorized-profile-20260920/report.json)
+A [fresh-cache profile of the revised recipe](../astro-site/public/evidence/membership-vectorized-profile-20260920/report.json)
 used the same frozen inputs and thread settings as the first profile. Its counts,
 fitted-normalizer fingerprint and resident input/target fingerprints match the
 baseline exactly: 34,474 training contexts in 1,078 batches, with ten contexts in
diff --git a/docs/licenses/ambiq-device-model-license-draft.md b/docs-maintainers/licenses/ambiq-device-model-license-draft.md
similarity index 98%
rename from docs/licenses/ambiq-device-model-license-draft.md
rename to docs-maintainers/licenses/ambiq-device-model-license-draft.md
index 2816d31..ef6f9a2 100644
--- a/docs/licenses/ambiq-device-model-license-draft.md
+++ b/docs-maintainers/licenses/ambiq-device-model-license-draft.md
@@ -83,5 +83,5 @@ and commercial permissions in plain language, with `license: other` in Hugging
 Face metadata. Source code keeps its own BSD-3-Clause notice.
 
 This draft has no automatic application through inclusion in the sleepKIT
-repository. See the [model licensing policy](../model-licensing-policy.md) for
+repository. See the [model licensing policy](../../astro-site/src/content/docs/model-licensing-policy.mdx) for
 selection criteria and the current CMIDSS exception.
diff --git a/docs/reusable-blocks.md b/docs-maintainers/reusable-blocks.md
similarity index 100%
rename from docs/reusable-blocks.md
rename to docs-maintainers/reusable-blocks.md
diff --git a/docs/shared-foundation.md b/docs-maintainers/shared-foundation.md
similarity index 98%
rename from docs/shared-foundation.md
rename to docs-maintainers/shared-foundation.md
index d2792af..acacc3b 100644
--- a/docs/shared-foundation.md
+++ b/docs-maintainers/shared-foundation.md
@@ -1,6 +1,6 @@
 # Shared architecture notes
 
-These maintainer notes describe shared architecture proposals and sleepKIT’s role as a consumer. For task workflows, start with the [task guides](tasks/index.md).
+These maintainer notes describe shared architecture proposals and sleepKIT’s role as a consumer. For task workflows, start with the [task guides](../astro-site/src/content/docs/tasks/index.mdx).
 
 Status: architecture proposal, 2026-09-20. This extends the sleepKIT refactor to
 heartKIT, sleepKIT and compressionKIT. It does not claim the shared APIs, backend
@@ -180,8 +180,8 @@ bundles retain appropriate hashes and aggregate evidence, with private data-acce
 records and subject-level evidence kept separately. A golden release is promoted by
 passing these contracts, not by inheriting a runner class.
 
-The first concrete sleepKIT consumer now has a [golden reconstruction definition](detection-golden.md)
-and a [stage profiler](detection-profiling.md). These preserve the existing TensorFlow
+The first concrete sleepKIT consumer now has a [golden reconstruction definition](../astro-site/src/content/docs/detection-golden.mdx)
+and a [stage profiler](../astro-site/src/content/docs/detection-profiling.mdx). These preserve the existing TensorFlow
 recipe and finite sampling contract. They do not yet establish cross-backend quality
 reproduction, loader scaling or a shared heliaEDGE API.
 
diff --git a/docs/assets/tasks/apnea/apnea-classes.md b/docs/assets/tasks/apnea/apnea-classes.md
deleted file mode 100644
index e118e36..0000000
--- a/docs/assets/tasks/apnea/apnea-classes.md
+++ /dev/null
@@ -1,8 +0,0 @@
-| CLASS   | LABEL | DESCRIPTION |
-| ------- | ----- | ----------- |
-| NONE    | 0     | No apnea    |
-| HYPOPNEA| 1     | Shallow breathing |
-| CENTRAL | 2     | Central apnea |
-| OBSTRUCTIVE | 3 | Obstructive apnea |
-| MIXED   | 4     | Mixed apnea |
-| NOISE   | 5     | Noise |
diff --git a/docs/assets/tasks/detect/detect-default-class-table.md b/docs/assets/tasks/detect/detect-default-class-table.md
deleted file mode 100644
index e9c61cb..0000000
--- a/docs/assets/tasks/detect/detect-default-class-table.md
+++ /dev/null
@@ -1,4 +0,0 @@
-| CLASS   | LABELS          |
-| ------- | --------------- |
-| 0       | AWAKE           |
-| 1       | SLEEP           |
diff --git a/docs/assets/tasks/stage/stage-default-class-table.md b/docs/assets/tasks/stage/stage-default-class-table.md
deleted file mode 100644
index a319e15..0000000
--- a/docs/assets/tasks/stage/stage-default-class-table.md
+++ /dev/null
@@ -1,9 +0,0 @@
-| CLASS   | LABELS          |
-| ------- | --------------- |
-| 0       | WAKE            |
-| 1       | STAGE1 (N1)     |
-| 2       | STAGE2 (N2)     |
-| 3       | STAGE3 (N3)     |
-| 4       | STAGE4 (N4)     |
-| 5       | REM             |
-| 6       | NOISE           |
diff --git a/docs/assets/usage/json-configuration.md b/docs/assets/usage/json-configuration.md
deleted file mode 100644
index be1433e..0000000
--- a/docs/assets/usage/json-configuration.md
+++ /dev/null
@@ -1,95 +0,0 @@
-```json
-{
-    "name": "sd-2-tcn-sm",
-    "job_dir": "./results/sd-2-tcn-sm",
-    "verbose": 2,
-
-    "datasets": [{
-        "name": "cmidss",
-        "params": {
-            "path": "./datasets/cmidss"
-        }
-    }],
-
-    "feature": {
-        "name": "FS-W-A-5",
-        "sampling_rate": 0.2,
-        "frame_size": 12,
-        "loader": "hdf5",
-        "feat_key": "features",
-        "label_key": "detect_labels",
-        "mask_key": "mask",
-        "feat_cols": null,
-        "save_path": "./datasets/store/fs-w-a-5-60",
-        "params": {}
-    },
-
-    "sampling_rate": 0.0083333,
-    "frame_size": 240,
-
-    "num_classes": 2,
-    "class_map": {
-        "0": 0,
-        "1": 1,
-        "2": 1,
-        "3": 1,
-        "4": 1,
-        "5": 1
-    },
-    "class_names": ["WAKE", "SLEEP"],
-
-    "samples_per_subject": 100,
-    "val_samples_per_subject": 100,
-    "test_samples_per_subject": 50,
-
-    "val_size": 4000,
-    "test_size": 2500,
-
-    "val_subjects": 0.20,
-    "batch_size": 128,
-    "buffer_size": 10000,
-    "epochs": 200,
-    "steps_per_epoch": 25,
-    "val_steps_per_epoch": 25,
-    "val_metric": "loss",
-    "lr_rate": 1e-3,
-    "lr_cycles": 1,
-    "label_smoothing": 0,
-
-    "test_metric": "f1",
-    "test_metric_threshold": 0.02,
-    "tflm_var_name": "sk_detect_flatbuffer",
-    "tflm_file": "sk_detect_flatbuffer.h",
-
-    "backend": "pc",
-    "display_report": true,
-
-    "quantization": {
-        "qat": false,
-        "mode": "INT8",
-        "io_type": "int8",
-        "concrete": true,
-        "debug": false
-    },
-
-    "model_file": "model.keras",
-    "use_logits": false,
-    "architecture": {
-        "name": "tcn",
-        "params": {
-            "input_kernel": [1, 5],
-            "input_norm": "batch",
-            "blocks": [
-                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
-            ],
-            "output_kernel": [1, 5],
-            "include_top": true,
-            "use_logits": true,
-            "model_name": "tcn"
-        }
-    }
-}
-```
diff --git a/docs/assets/usage/python-configuration.md b/docs/assets/usage/python-configuration.md
deleted file mode 100644
index a05b7bf..0000000
--- a/docs/assets/usage/python-configuration.md
+++ /dev/null
@@ -1,97 +0,0 @@
-```py linenums="1"
-
-sk.TaskParams(
-    name="sd-2-tcn-sm",
-    job_dir="./results/sd-2-tcn-sm",
-    verbose=2,
-
-    datasets=[
-        sk.NamedParams(
-            name="cmidss",
-            params={
-                "path": "./datasets/cmidss"
-            }
-        )
-    ],
-
-    feature=sk.FeatureParams(
-        name="FS-W-A-5",
-        sampling_rate=0.2,
-        frame_size=12,
-        loader="hdf5",
-        feat_key="features",
-        label_key="detect_labels",
-        mask_key="mask",
-        feat_cols=None,
-        save_path="./datasets/store/fs-w-a-5-60",
-        params={}
-    ),
-
-    sampling_rate=0.0083333,
-    frame_size=240,
-
-    num_classes=2,
-    class_map={
-        0: 0,
-        1: 1,
-        2: 1,
-        3: 1,
-        4: 1,
-        5: 1
-    },
-    class_names=["WAKE", "SLEEP"],
-
-    samples_per_subject=100,
-    val_samples_per_subject=100,
-    test_samples_per_subject=50,
-
-    val_size=4000,
-    test_size=2500,
-
-    val_subjects=0.20,
-    batch_size=128,
-    buffer_size=10000,
-    epochs=200,
-    steps_per_epoch=25,
-    val_steps_per_epoch=25,
-    val_metric="loss",
-    lr_rate=1e-3,
-    lr_cycles=1,
-    label_smoothing=0,
-
-    test_metric="f1",
-    test_metric_threshold=0.02,
-    tflm_var_name="sk_detect_flatbuffer",
-    tflm_file="sk_detect_flatbuffer.h",
-
-    backend="pc",
-    display_report=True,
-
-    quantization=sk.QuantizationParams(
-        qat=False,
-        mode="INT8",
-        io_type="int8",
-        concrete=True,
-        debug=False
-    ),
-
-    model_file="model.keras",
-    use_logits=False,
-    architecture=sk.NamedParams(
-        name="tcn",
-        params={
-            "input_kernel": [1, 5],
-            "input_norm": "batch",
-            "blocks": [
-                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
-            ],
-            "output_kernel": [1, 5],
-            "include_top": True,
-            "use_logits": True,
-            "model_name": "tcn"
-        }
-    )
-```
diff --git a/docs/assets/zoo/apnea/apnea-model-hw-table.md b/docs/assets/zoo/apnea/apnea-model-hw-table.md
deleted file mode 100644
index fdfdfda..0000000
--- a/docs/assets/zoo/apnea/apnea-model-hw-table.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| Name            | Params   | FLOPS   | Metric     | Time     | Arena | Energy     |
-| --------------- | -------- | ------- | ---------- | -------- | ----- | ---------- |
-| __SA-2-TCN-SM__ | 10K      | 44M/hr  | 91.0% F1   | 1.8s/hr  |  61KB |  17.0mJ/hr |
diff --git a/docs/assets/zoo/apnea/apnea-model-zoo-table.md b/docs/assets/zoo/apnea/apnea-model-zoo-table.md
deleted file mode 100644
index f0060c2..0000000
--- a/docs/assets/zoo/apnea/apnea-model-zoo-table.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME             | LOCATION | # CLASSES | MODEL   | PARAMS | FLOPS   | ACCURACY | F1    |
-| ---------------- | -------- | --------- | ------- | ------ | ------- | -------- | ----- |
-| __SA-2-TCN-SM__  | Wrist    | 2         | TCN     | 5K     | 20M/hr  | 91.0%    | 91.0% |
diff --git a/docs/assets/zoo/detect/detect-model-hw-table.md b/docs/assets/zoo/detect/detect-model-hw-table.md
deleted file mode 100644
index 6ab32fc..0000000
--- a/docs/assets/zoo/detect/detect-model-hw-table.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| Name            | Params   | FLOPS   | Metric     | Time    | Arena | Energy     |
-| --------------- | -------- | ------- | ---------- | ------- | ----- | ---------- |
-| __SD-2-TCN-SM__ | 9K       | 1.5M/hr | 94.5% AP   | 61ms/hr |  34KB | 586uJ/hr   |
diff --git a/docs/assets/zoo/detect/detect-model-zoo-table.md b/docs/assets/zoo/detect/detect-model-zoo-table.md
deleted file mode 100644
index 281f90c..0000000
--- a/docs/assets/zoo/detect/detect-model-zoo-table.md
+++ /dev/null
@@ -1,3 +0,0 @@
-| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
-| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
-| __SD-2-TCN-SM__  | Wrist    | 2         | TCN   | 9K     | 1.5M/hr | 95.9%    | 94.5% |
diff --git a/docs/assets/zoo/detect/sleep-detect-2-config.md b/docs/assets/zoo/detect/sleep-detect-2-config.md
deleted file mode 100644
index 7e70129..0000000
--- a/docs/assets/zoo/detect/sleep-detect-2-config.md
+++ /dev/null
@@ -1,69 +0,0 @@
-```json
-{
-    "name": "sleep-detect-2",
-    "job_dir": "./results/sleep-detect-2",
-    "ds_path": "./datasets/store/fs-w-a-5-60/cmidss",
-    "dataset": {
-        "name": "hdf5",
-        "params": {
-            "feat_key": "features",
-            "label_key": "detect_labels",
-            "mask_key": "mask"
-        }
-    },
-    "sampling_rate": 0.01666666,
-    "frame_size": 240,
-    "model_file": "model.keras",
-    "num_classes": 2,
-    "class_map": {
-        "0": 0,
-        "1": 1,
-        "2": 1,
-        "3": 1,
-        "4": 1,
-        "5": 1
-    },
-    "class_names": ["WAKE", "SLEEP"],
-    "samples_per_subject": 100,
-    "val_samples_per_subject": 100,
-    "val_subjects": 0.20,
-    "val_size": 4000,
-    "val_acc_threshold": 0.98,
-    "test_size": 1000,
-    "test_samples_per_subject": 20,
-    "batch_size": 128,
-    "buffer_size": 10000,
-    "epochs": 200,
-    "steps_per_epoch": 25,
-    "val_metric": "loss",
-    "lr_rate": 1e-3,
-    "lr_cycles": 1,
-    "label_smoothing": 0,
-    "backend": "pc",
-    "quantization": {
-        "enabled": true,
-        "qat": false,
-        "ptq": true,
-        "input_type": "int8",
-        "output_type": "int8",
-        "supported_ops": null
-    },
-    "architecture": {
-        "name": "tcn",
-        "params": {
-            "input_kernel": [1, 5],
-            "input_norm": "batch",
-            "blocks": [
-                {"depth": 1, "branch": 1, "filters": 16, "kernel": [1, 5], "dilation": [1, 1], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 32, "kernel": [1, 5], "dilation": [1, 2], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 48, "kernel": [1, 5], "dilation": [1, 4], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"},
-                {"depth": 1, "branch": 1, "filters": 64, "kernel": [1, 5], "dilation": [1, 8], "dropout": 0.10, "ex_ratio": 1, "se_ratio": 4, "norm": "batch"}
-            ],
-            "output_kernel": [1, 5],
-            "include_top": true,
-            "use_logits": true,
-            "model_name": "tcn"
-        }
-    }
-}
-```
diff --git a/docs/assets/zoo/stage/stage-model-hw-table.md b/docs/assets/zoo/stage/stage-model-hw-table.md
deleted file mode 100644
index 56314f4..0000000
--- a/docs/assets/zoo/stage/stage-model-hw-table.md
+++ /dev/null
@@ -1,6 +0,0 @@
-| Name            | Params   | FLOPS   | Metric     | Time     | Arena | Energy    |
-| --------------- | -------- | ------- | ---------- | -------- | ----- | --------- |
-| SD-2-TCN-SM     | 10K      | 1.7M/hr | 96.0% AP   | 66ms/hr  | 32KB  |  705uJ/hr |
-| SD-3-TCN-SM     | 11K      | 1.9M/hr | 91.8% AP   | 75ms/hr  | 34KB  |  710uJ/hr |
-| SD-4-TCN-SM     | 26K      | 4.2M/hr | 81.6% AP   | 129ms/hr | 47KB  | 1.23mJ/hr |
-| SD-5-TCN-SM     | 28K      | 4.6M/hr | 74.1% AP   | 172ms/hr | 50KB  | 1.65mJ/hr |
diff --git a/docs/assets/zoo/stage/stage-model-zoo-table.md b/docs/assets/zoo/stage/stage-model-zoo-table.md
deleted file mode 100644
index 8b82574..0000000
--- a/docs/assets/zoo/stage/stage-model-zoo-table.md
+++ /dev/null
@@ -1,6 +0,0 @@
-| NAME             | LOCATION | # CLASSES | MODEL | PARAMS | FLOPS   | ACCURACY | AP    |
-| ---------------- | -------- | --------- | ----- | ------ | ------- | -------- | ----- |
-| SS-2-TCN-SM      | Wrist    | 2         | TCN   | 10K    | 1.7M/hr | 88.9%    | 96.0% |
-| SS-3-TCN-SM      | Wrist    | 3         | TCN   | 11K    | 1.9M/hr | 84.4%    | 91.8% |
-| SS-4-TCN-SM      | Wrist    | 4         | TCN   | 26K    | 4.2M/hr | 75.5%    | 81.6% |
-| SS-5-TCN-SM      | Wrist    | 5         | TCN   | 28K    | 4.6M/hr | 68.4%    | 74.1% |
diff --git a/docs/css/custom.css b/docs/css/custom.css
deleted file mode 100644
index e748c0f..0000000
--- a/docs/css/custom.css
+++ /dev/null
@@ -1,208 +0,0 @@
-.termynal-comment {
-  color: #4a968f;
-  font-style: italic;
-  display: block;
-}
-
-.termy {
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-          to make the mark be on the same line as the link
-      */
-  content: "\00A0[↪]";
-}
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-a.internal-link::after {
-  /* \00A0 is a non-breaking space
-          to make the mark be on the same line as the link
-      */
-  content: "\00A0↪";
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-.shadow {
-  box-shadow: 5px 5px 10px #999;
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-.md-grid {
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-/* Give space to lower icons so Gitter chat doesn't get on top of them */
-.md-footer-meta {
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-  margin: 1em;
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-.user .avatar-wrapper {
-  width: 80px;
-  height: 80px;
-  margin: 10px auto;
-  overflow: hidden;
-  border-radius: 50%;
-  position: relative;
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-.user .avatar-wrapper img {
-  position: absolute;
-  top: 50%;
-  left: 50%;
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-.user .title {
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-.user .count {
-  font-size: 80%;
-  text-align: center;
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-
-a.announce-link:link,
-a.announce-link:visited {
-  color: #fff;
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-a.announce-link:hover {
-  color: var(--md-accent-fg-color);
-}
-
-.announce-wrapper {
-  display: flex;
-  justify-content: space-between;
-  flex-wrap: wrap;
-  align-items: center;
-}
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-.announce-wrapper div.item {
-  display: none;
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-.announce-wrapper .sponsor-badge {
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-  position: absolute;
-  top: -10px;
-  right: 0;
-  font-size: 0.5rem;
-  color: #999;
-  background-color: #666;
-  border-radius: 10px;
-  padding: 0 10px;
-  z-index: 10;
-}
-
-.announce-wrapper .sponsor-image {
-  display: block;
-  border-radius: 20px;
-}
-
-.announce-wrapper>div {
-  min-height: 40px;
-  display: flex;
-  align-items: center;
-}
-
-.twitter {
-  color: #00acee;
-}
-
-/* Right to left languages */
-code {
-  direction: ltr;
-  display: inline-block;
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-.md-content__inner h1 {
-  direction: ltr !important;
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-  margin-top: 2em;
-  margin-bottom: 2em;
-}
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-@keyframes heart {
-  0%, 40%, 80%, 100% {
-    transform: scale(1);
-  }
-  20%, 60% {
-    transform: scale(1.15);
-  }
-}
-.heart {
-  animation: heart 1000ms infinite;
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-
-th, td {
-  border: 1px solid var(--md-typeset-table-color);
-  border-spacing: 0;
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-  border-left: none;
-  border-top: none;
-}
-
-.md-typeset__table {
-  line-height: 1;
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-.md-typeset__table table:not([class]) {
-  font-size: .74rem;
-  border-right: none;
-}
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-.md-typeset__table table:not([class]) th {
-  padding: 9px;
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-
-/* light mode alternating table bg colors */
-.md-typeset__table tr:nth-child(2n) {
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-
-/* dark mode alternating table bg colors */
-[data-md-color-scheme="slate"] .md-typeset__table tr:nth-child(2n) {
-  background-color: hsla(var(--md-hue),25%,25%,1)
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-
-.sk-plotly-graph-div {
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-.md-typeset h2 {
-  color: #E92063;
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diff --git a/docs/css/mkdocstrings.css b/docs/css/mkdocstrings.css
deleted file mode 100644
index 1fdf59b..0000000
--- a/docs/css/mkdocstrings.css
+++ /dev/null
@@ -1,4 +0,0 @@
-div.doc-contents {
-    padding-left: 25px;
-    border-left: .05rem solid var(--md-typeset-table-color);
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diff --git a/docs/css/termynal.css b/docs/css/termynal.css
deleted file mode 100644
index 6240ab5..0000000
--- a/docs/css/termynal.css
+++ /dev/null
@@ -1,109 +0,0 @@
-/**
- * termynal.js
- *
- * @author Ines Montani <ines@ines.io>
- * @version 0.0.1
- * @license MIT
- */
-
- :root {
-    --color-bg: #252a33;
-    --color-text: #eee;
-    --color-text-subtle: #a2a2a2;
-}
-
-[data-termynal] {
-    width: 750px;
-    max-width: 100%;
-    background: var(--color-bg);
-    color: var(--color-text);
-    /* font-size: 18px; */
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-    border-radius: 4px;
-    padding: 75px 45px 35px;
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-    display: block;
-    color: #aebbff;
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diff --git a/docs/datasets/index.md b/docs/datasets/index.md
deleted file mode 100644
index c5e27f1..0000000
--- a/docs/datasets/index.md
+++ /dev/null
@@ -1,33 +0,0 @@
-
-# :material-database: Datasets
-
-sleepKIT provides support for a number of datasets to facilitate training the __sleep-monitoring tasks__. Most of the datasets are readily available and can be downloaded and used for training and evaluation. The datasets inherit from [Dataset](/sleepkit/api/sleepkit/datasets/dataset) and can be accessed either directly or through the factory singleton [`sk.DatasetFactory`](#dataset-factory).
-
-
-## Available Datasets
-
-Below is a list of the currently available datasets in sleepKIT. Please make sure to review each dataset's license for terms and limitations.
-
-* **[MESA](./mesa.md)**: A longitudinal investigation of factors associated with the development of subclinical cardiovascular disease and the progression of subclinical to clinical cardiovascular disease in 6,814 black, white, Hispanic, and Chinese
-
-* **[CMIDSS](./cmidss.md)**: The Child Mind Institute - Detect Sleep States (CMIDSS) dataset comprises 300 subjects with over 500 multi-day recordings of wrist-worn accelerometer data annotated with two event types: onset, the beginning of sleep, and wakeup, the end of sleep.
-
-* **[YSYW](./ysyw.md)**: A total of 1,983 PSG recordings were provided by the Massachusetts General Hospital’s (MGH) Sleep Lab in the Sleep Division together with the Computational Clinical Neurophysiology Laboratory, and the Clinical Data Ani- mation Center.
-
-* **[STAGES](./stages.md)**: The Stanford Technology Analytics and Genomics in Sleep (STAGES) study is a prospective cross-sectional, multi-site study involving 20 data collection sites from six centers including Stanford University, Bogan Sleep Consulting, Geisinger Health, Mayo Clinic, MedSleep, and St. Luke's Hospital.
-
-* **[Bring-Your-Own-Data](./byod.md)**: Add new datasets to sleepKIT by providing your own data. Subclass `Dataset` and register it with the `sk.DatasetFactory`.
-
----
-
-## Dataset Factory
-
-The dataset factory, `sk.DatasetFactory`, provides a convenient way to access the datasets. The factory is a thread-safe singleton class that provides a single point of access to the datasets via the datasets' slug names. The benefit of using the factory is it allows registering new additional datasets that can then be leveraged by existing and new tasks.
-
-The dataset factory provides the following methods:
-
-* **sk.DatasetFactory.register**: Register a custom dataset
-* **sk.DatasetFactory.unregister**: Unregister a custom dataset
-* **sk.DatasetFactory.has**: Check if a dataset is registered
-* **sk.DatasetFactory.get**: Get a dataset
-* **sk.DatasetFactory.list**: List all available datasets
diff --git a/docs/features/index.md b/docs/features/index.md
deleted file mode 100644
index f958844..0000000
--- a/docs/features/index.md
+++ /dev/null
@@ -1,70 +0,0 @@
-# :simple-chartdotjs: Features
-
-__sleepKIT__ includes a mini _feature store_ to enable crafting rich feature sets to train and evaluate models. The store includes several built-in feature set generators that can be invoked to create feature sets for a variety of use cases. Each feature set inherits from `sk.FeatureSet` and provides implementations for generating features from the dataset.
-
-The main method, `generate_subject_features` receives the target dataset name, subject id, and high-level `sk.TaskParams` which includes field `feature` which includes feature set specific parameters. The resulting data and labels are stored in HDF5 format that can then be used for training via `sk.H5Dataloader` class. Custom feature set generators can also be added to the feature store by subclassing `sk.FeatureSet` and registering the new generator with the feature store.
-
-<div class="annotate" markdown>
-
-1. Load the configuration data (e.g. `configuration.json`)
-1. Load datasets from DatasetFactory (e.g. `mesa`)
-1. Load feature generator from FeatureFactory (e.g. `FS-W-PA-14`)
-1. Generate features for each dataset/subject (e.g. `generate_subject_features`)
-1. Save subjects features to disk (e.g. `subject.h5`)
-
-</div>
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-<br/>
-
-```mermaid
-graph TD
-A("`Load
-configuration
-__TaskParams__
-`")
-B("`Load datasets
-__DatasetFactory__
-`")
-C("`Load feature
-generator
-__FeatureFactory__
-`")
-D("`Generate
-features
-`")
-E("`Save
-features
-`")
-
-A ==> B
-B ==> C
-C ==> D
-D ==> E
-
-```
-
----
-
-## Available Feature Sets
-
-- **[FS-W-PA-14](./fs_w_pa_14.md)**:__14__ features derived from __PPG__ and __IMU__ on __wrist__ for __sleep stage__ classification.
-- **[FS-C-EAR-9](./fs_c_ear_9.md)**: __9__ features derived from __ECG__, __RSP__, and __IMU__ on __chest__ for __sleep stage__ classification.
-- **[FS-W-A-5](./fs_w_a_5.md)**: __5__ features derived from __IMU__ on __wrist__ for actigraphy style __sleep detection__.
-- **[FS-H-E-10](./fs_h_e_10.md)**: __10__ features derived from __ECG__ and __EOG__ on __head__ for __sleep stage__ classification.
-- **[FS-W-P-5](./fs_w_p_5.md)**: __5__ features derived from __PPG__ on __wrist__ for __sleep apnea__ classification.
-- **[BYOFS](./byofs.md)**: Bring-Your-Own-Feature-Set (BYOFS) to create custom feature sets.
-
-## Feature Factory
-
-The feature factory, `sk.FeatureFactory`, provides a convenient way to access the feature sets. The factory is a thread-safe singleton class that provides a single point of access to the features via the features' slug names. The benefit of using the factory is it allows registering new additional feature sets that can then be leveraged by existing and new tasks.
-
-The feature factory provides the following methods:
-
-* **sk.FeatureFactory.register**: Register a custom feature set
-* **sk.FeatureFactory.unregister**: Unregister a custom feature set
-* **sk.FeatureFactory.has**: Check if a feature set is registered
-* **sk.FeatureFactory.get**: Get a feature set
-* **sk.FeatureFactory.list**: List all available feature sets
diff --git a/docs/guides/index.md b/docs/guides/index.md
deleted file mode 100644
index 6b1552f..0000000
--- a/docs/guides/index.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# User guide
-
-Build a sleepKIT workflow from datasets, feature extraction and model architectures. Use the guides below to configure each step, then follow a tutorial for a complete experiment.
-
-## Run an experiment
-
-Start with a task configuration and work through the modes you need.
-
-[Configure a run](../modes/configuration.md){ .md-button }
-[Download data](../modes/download.md){ .md-button }
-[Train](../modes/train.md){ .md-button }
-[Evaluate](../modes/evaluate.md){ .md-button }
-[Export](../modes/export.md){ .md-button }
-[Run a demo](../modes/demo.md){ .md-button }
-
-## Choose your components
-
-- **Datasets** provide recordings and labels for your task. Review each dataset's access requirements and terms.
-- **Feature sets** transform recordings into inputs for training and evaluation.
-- **Model architectures** define the network and its configurable parameters.
-
-[Browse datasets](../datasets/index.md){ .md-button }
-[Explore feature sets](../features/index.md){ .md-button }
-[Choose an architecture](../models/index.md){ .md-button }
-
-## Tutorials and experiments
-
-Train a sleep-detection model from wrist IMU data in the saved notebook, or explore how feature and model choices affect sleep staging.
-
-[Detection training notebook](train-detect-model.ipynb){ .md-button }
-[Staging ablation study](stage-ablation.md){ .md-button }
diff --git a/docs/js/custom.js b/docs/js/custom.js
deleted file mode 100644
index af9397c..0000000
--- a/docs/js/custom.js
+++ /dev/null
@@ -1,144 +0,0 @@
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diff --git a/docs/js/termynal.js b/docs/js/termynal.js
deleted file mode 100644
index 4ac3270..0000000
--- a/docs/js/termynal.js
+++ /dev/null
@@ -1,264 +0,0 @@
-/**
- * termynal.js
- * A lightweight, modern and extensible animated terminal window, using
- * async/await.
- *
- * @author Ines Montani <ines@ines.io>
- * @version 0.0.1
- * @license MIT
- */
-
-'use strict';
-
-/** Generate a terminal widget. */
-class Termynal {
-    /**
-     * Construct the widget's settings.
-     * @param {(string|Node)=} container - Query selector or container element.
-     * @param {Object=} options - Custom settings.
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-     * @param {boolean} options.noInit - Don't initialise the animation.
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-        this.container.appendChild(restart)
-        this.container.setAttribute('data-termynal', '');
-    }
-
-    /**
-     * Initialise the widget, get lines, clear container and start animation.
-     */
-    init() {
-        /**
-         * Calculates width and height of Termynal container.
-         * If container is empty and lines are dynamically loaded, defaults to browser `auto` or CSS.
-         */
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-        this.container.style.width = containerStyle.width !== '0px' ?
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-        this.container.style.minHeight = containerStyle.height !== '0px' ?
-            containerStyle.height : undefined;
-
-        this.container.setAttribute('data-termynal', '');
-        this.container.innerHTML = '';
-        for (let line of this.lines) {
-            line.style.visibility = 'visible'
-        }
-        this.start();
-    }
-
-    /**
-     * Start the animation and rener the lines depending on their data attributes.
-     */
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-        this.addFinish()
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-
-        for (let line of this.lines) {
-            const type = line.getAttribute(this.pfx);
-            const delay = line.getAttribute(`${this.pfx}-delay`) || this.lineDelay;
-
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-                line.setAttribute(`${this.pfx}-cursor`, this.cursor);
-                await this.type(line);
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-    }
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-     * Animate a typed line.
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-     */
-    async type(line) {
-        const chars = [...line.textContent];
-        line.textContent = '';
-        this.container.appendChild(line);
-
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-            await this._wait(delay);
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-
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-     * Animate a progress bar.
-     * @param {Node} line - The line element to render.
-     */
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-            || this.progressLength;
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-            || this.progressChar;
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-			|| this.progressPercent;
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-/**
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-*/
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-    containers.split('|')
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diff --git a/docs/modes/download.md b/docs/modes/download.md
deleted file mode 100644
index 0be3d47..0000000
--- a/docs/modes/download.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# :material-download: Download Datasets
-
-The `download` command is used to download all datasets specified. Please refer to [Datasets](../datasets/index.md) for details on the available datasets. Additional datasets can be added by creating a new dataset class and registering it with __sleepKIT__ dataset factory.
-
-## Usage
-
-### CLI
-
-Using the CLI, the `download` command can be used to download specified datasets in the configuration file or directly in the command line.
-
-```bash
-sleepkit -m download -c '{"datasets": [{"name": "cmidss", "parameters": {"path": ".datatasets/cmidss"}}]}'
-```
-
-### Python
-
-In code, the `download` method of a dataset can be used to download the dataset.
-
-```py linenums="1"
-import sleepkit as sk
-
-ds = sk.DatasetFactory.get("cmidss")(path="./datasets/cmidss")
-ds.download()
-
-```
-
-## Arguments 
-
-Please refer to [TaskParams](../modes/configuration.md#taskparams) for the list of arguments that can be used with the `download` command.
-
----
diff --git a/docs/modes/evaluate.md b/docs/modes/evaluate.md
deleted file mode 100644
index 45025ac..0000000
--- a/docs/modes/evaluate.md
+++ /dev/null
@@ -1,93 +0,0 @@
-# :material-ab-testing: Model Evaluation
-
-## Introduction 
-
-Evaluate mode is used to test the performance of the model on the reserved test set for the specified task. Similar to training, the routine can be customized via CLI configuration file or by setting the parameters directly in the code. The evaluation process involves testing the model's performance on the test data to measure its accuracy, precision, recall, and F1 score. A number of results and metrics will be generated and saved to the `job_dir`.
-
-<div class="annotate" markdown>
-
-1. Load the configuration data (e.g. `configuration.json`)
-1. Load features (e.g. `FS-W-A-5`)
-1. Load the trained model (e.g. `model.keras`)
-1. Define the metrics (e.g. `accuracy`)
-1. Evaluate the model (e.g. `model.evaluate`)
-1. Generate evaluation report (e.g. `report.json`)
-
-</div>
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-
-```mermaid
-graph TD
-A("`Load
-configuration
-__TaskParams__
-`")
-B("`Load
-features
-__FeatureFactory__
-`")
-C("`Load trained
-__model__
-`")
-D("`Define
-__metrics__
-`")
-E("`Evaluate
-__model__
-`")
-F("`Generate
-__report__
-`")
-A ==> B
-B ==> C
-subgraph CF["Evaluate"]
-    C ==> D
-    D ==> E
-end
-E ==> F
-
-```
-
----
-
-## Usage
-
-### CLI
-
-The following command will evaluate a detect model using the reference configuration.
-
-```bash
-sleepkit --task detect --mode evaluate --config ./configuration.json
-```
-
-### Python
-
-The model can be evaluated using the following snippet:
-
-```py linenums="1"
-from pathlib import Path
-import sleepkit as sk
-
-task = sk.TaskFactory.get("detect")
-
-params = sk.TaskParams.model_validate_json(
-    Path("configuration.json").read_text()
-)
-
-task.evaluate(params)
-
-```
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
----
-
-## Arguments 
-
-Please refer to [TaskParams](../modes/configuration.md#taskparams) for the list of arguments that can be used with the `evaluate` command.
-
----
diff --git a/docs/modes/export.md b/docs/modes/export.md
deleted file mode 100644
index 4d74f0e..0000000
--- a/docs/modes/export.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# :material-export: Model Exporting
-
-## Introduction 
-
-Export mode is used to convert the trained TensorFlow model into a format that can be used for deployment onto Ambiq's family of SoCs. Currently, the command will convert the TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file or by setting the `quantization` parameter in the code.
-
-<div class="annotate" markdown>
-
-1. Load the configuration data (e.g. `configuration.json`)
-1. Load the test data (e.g. `test.tfds`)
-1. Load the trained model (e.g. `model.keras`)
-1. Quantize the model (e.g. `16x8`)
-1. Convert the model (e.g. `TFL`, `TFLM`)
-1. Verify the models' outputs match
-1. Save artifacts (e.g. `model.tflite`)
-
-</div>
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-
-```mermaid
-graph TD
-A("`Load
-configuration
-__TaskParams__
-`")
-B("`Load test
-data
-`")
-C("`Load trained
-__model__
-`")
-D("`Quantize
-__model__
-`")
-E("`Convert
-__model__
-`")
-F("`Verify
-__outputs__
-`")
-G("`Save
-__artifacts__
-`")
-A ==> B
-B ==> C
-subgraph CF["Export"]
-    C ==> D
-    D ==> E
-    E ==> F
-end
-F ==> G
-
-```
-
----
-
-## Usage
-
-### CLI
-
-The following command will export a detect model using the reference configuration.
-
-```bash
-sleepkit --task detect --mode export --config ./configuration.json
-```
-
-### Python
-
-The model can be exported using the following snippet:
-
-```py linenums="1"
-from pathlib import Path
-import sleepkit as sk
-
-task = sk.TaskFactory.get("detect")
-
-params = sk.TaskParams.model_validate_json(
-    Path("configuration.json").read_text()
-)
-
-task.export(params)
-
-```
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
----
-
-## Arguments 
-
-Please refer to [TaskParams](../modes/configuration.md#taskparams) for the list of arguments that can be used with the `export` command.
-
----
diff --git a/docs/modes/train.md b/docs/modes/train.md
deleted file mode 100644
index 598a0aa..0000000
--- a/docs/modes/train.md
+++ /dev/null
@@ -1,109 +0,0 @@
-# :material-chart-ppf: Model Training
-
-## Introduction 
-
-Each task provides a mode to train a model on the specified features. The training mode can be invoked either via CLI or within `sleepkit` python package. At a high level, the training mode performs the following actions based on the provided configuration parameters:
-
-<div class="annotate" markdown>
-
-1. Load the configuration data (e.g. `configuration.json`)
-1. Load features (e.g. `FS-W-A-5`)
-1. Initialize custom model architecture (e.g. `tcn`)
-1. Define the metrics, loss, and optimizer (e.g. `accuracy`, `categorical_crossentropy`, `adam`)
-1. Train the model (e.g. `model.fit`)
-1. Save artifacts (e.g. `model.keras`)
-
-</div>
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-<br/>
-
-```mermaid
-graph TD
-A("`Load
-configuration
-__TaskParams__
-`")
-B("`Load
-features
-__FeatureFactory__
-`")
-C("`Initialize
-model
-__ModelFactory__
-`")
-D("`Define
-_metrics_, _loss_,
-_optimizer_
-`")
-E("`Train
-__model__
-`")
-F("`Save
-__artifacts__
-`")
-A ==> B
-B ==> C
-subgraph "Model Training"
-    C ==> D
-    D ==> E
-end
-E ==> F
-```
-
----
-
-## Usage
-
-### CLI
-
-The following command will train a sleep stage model using the reference configuration.
-
-```bash
-sleepkit --task stage --mode train --config ./configuration.json
-```
-
-### Python
-
-The model can be trained using the following snippet:
-
-```py linenums="1"
-
-from pathlib import Path
-import sleepkit as sk
-
-task = sk.TaskFactory.get("detect")
-
-params = sk.TaskParams.model_validate_json(
-    Path("configuration.json").read_text()
-)
-
-task.train(params)
-```
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
----
-
-## Arguments 
-
-Please refer to [TaskParams](../modes/configuration.md#taskparams) for the list of arguments that can be used with the `train` command.
-
----
-
-
-## Logging
-
-__sleepKIT__ provides built-in support for logging to several third-party services including [Weights & Biases](https://wandb.ai/site) (WANDB) and [TensorBoard](https://www.tensorflow.org/tensorboard).
-
-### WANDB
-
-The training mode is able to log all metrics and artifacts (aka models) to [Weights & Biases](https://wandb.ai/site) (WANDB). To enable WANDB logging, simply set environment variable `WANDB=1`. Remember to sign in prior to running experiments by running `wandb login`.
-
-
-### TensorBoard
-
-The training mode is able to log all metrics to [TensorBoard](https://www.tensorflow.org/tensorboard). To enable TensorBoard logging, simply set environment variable `TENSORBOARD=1`.
diff --git a/docs/quickstart.md b/docs/quickstart.md
deleted file mode 100644
index 703d6ff..0000000
--- a/docs/quickstart.md
+++ /dev/null
@@ -1,155 +0,0 @@
-# :material-clock-fast: Quickstart
-
-## Install sleepKIT
-
-We provide several installation methods including pip, uv, and Docker. Install __sleepKIT__ via pip/uv for the latest stable release or by cloning the GitHub repo for the most up-to-date. Additionally, a [VSCode Dev Container](https://code.visualstudio.com/docs/devcontainers/containers) is available and defined in [./.devcontainer](https://github.com/AmbiqAI/sleepkit/tree/main/.devcontainer) to run in an isolated Docker environment.
-
-!!! install
-
-    === "Git clone"
-
-        Clone the repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package. In this mode, uv is recommended.
-
-        ```bash
-        # Clone the repository
-        git clone https://github.com/AmbiqAI/sleepkit.git
-
-        # Navigate to the cloned directory
-        cd sleepkit
-
-        # Install the package in editable mode for development
-        uv sync
-        ```
-
-        When using editable mode via uv, be sure to activate the python environment: `source .venv/bin/activate`. <br>
-        On Windows using Powershell, use `.venv\Scripts\activate`.
-
-    === "PyPI install"
-
-        Install the sleepKIT package using pip or uv.
-        Visit the Python Package Index (PyPI) for more details on the package: [https://pypi.org/project/sleepkit/](https://pypi.org/project/sleepkit/)
-
-        ```bash
-        # Install with pip
-        pip install sleepkit
-        ```
-
-        Or, if you prefer to use uv, you can install the package with the following command:
-
-        ```bash
-        # Install with uv
-        uv add sleepkit
-        ```
-
-        Alternatively, you can install the latest development version directly from the GitHub repository. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @canary.
-
-        ```bash
-        pip install git+https://github.com/AmbiqAI/sleepkit.git@main
-        ```
-
-        Or, using uv:
-
-        ```bash
-        uv add git+https://github.com/AmbiqAI/sleepkit.git@main
-        ```
-
-## Requirements
-
-* [Python 3.12–3.13](https://www.python.org)
-* [uv](https://docs.astral.sh/uv/getting-started/installation/)
-
-Check the project's [pyproject.toml](https://github.com/AmbiqAI/sleepkit/blob/main/pyproject.toml) file for a list of up-to-date Python dependencies. Note that the installation methods above install all required dependencies. The following are optional dependencies only needed when running `demo` command using Ambiq's evaluation board (`EVB`) backend:
-
-* [Arm GNU Toolchain ^12.2](https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads)
-* [Segger J-Link ^7.92](https://www.segger.com/downloads/jlink/)
-
-Once installed, __sleepKIT__ can be used as either a CLI-based tool or as a Python package to perform advanced experimentation.
-
----
-
-## Use sleepKIT with CLI
-
-The sleepKIT command line interface (CLI) allows for simple single-line commands without writing Python code. The CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `sleepkit` command. Check out the [CLI Guide](./usage/cli.md) to learn more about available options.
-
-!!! example
-
-    === "Syntax"
-        `sleepkit` commands use the following syntax:
-
-        ```bash
-        sleepkit --mode [MODE] --task [TASK] --config [CONFIG]
-        ```
-
-        Or using short flags:
-
-        ```bash
-        sleepkit -m [MODE] -t [TASK] -c [CONFIG]
-        ```
-
-        Where:
-
-        * `MODE` is one of `download`, `feature`, `train`, `evaluate`, `export`, or `demo`
-        * `TASK` is one of `detect`, `stage`, or `apnea`
-        * `CONFIG` is configuration as JSON content or file path
-
-    === "Download"
-        Download datasets specified in the configuration file.
-
-        ```bash
-        sleepkit -m download -c ./configuration.json
-        ```
-    === "Feature"
-        Extract features from the datasets using the supplied configuration file.
-
-        ```bash
-        sleepkit -m feature -c ./configuration.json
-        ```
-
-    === "Train"
-        Train a sleep stage model using the supplied configuration file.
-
-        ```bash
-        sleepkit -m train -t stage -c ./configuration.json
-        ```
-
-    === "Evaluate"
-        Evaluate the trained sleep stage model using the supplied configuration file.
-
-        ```bash
-        sleepkit -m evaluate -t stage  -c ./configuration.json
-        ```
-
-    === "Demo"
-        Run demo on trained sleep stage model using the supplied configuration file.
-
-        ```bash
-        sleepkit -m demo -t stage -c ./configuration.json
-        ```
-
-## Use sleepKIT with Python
-
-The __sleepKIT__ Python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for a variety of tasks. The package is designed to be simple and easy to use.
-
-For example, you can create a custom model, train it, evaluate its performance on a validation set, and even export a quantized TensorFlow Lite model for deployment. Check out the [Python Guide](./usage/python.md) to learn more about using sleepKIT as a Python package.
-
-Load a task configuration, then call the steps you need. Replace `configuration.json` with a configuration for your dataset and model.
-
-```python title="run_task.py"
-from pathlib import Path
-import sleepkit as sk
-
-params = sk.TaskParams.model_validate_json(
-    Path("configuration.json").read_text()
-)
-task = sk.TaskFactory.get("stage")
-
-task.download(params)
-task.feature(params)
-task.train(params)
-task.evaluate(params)
-task.export(params)
-```
-
-The configuration controls dataset locations, features, class mapping and training settings. This example shows the configuration structure; adapt its paths and task-specific values before running it.
-
---8<-- "assets/usage/json-configuration.md"
diff --git a/docs/tasks/apnea.md b/docs/tasks/apnea.md
deleted file mode 100644
index 637b1e7..0000000
--- a/docs/tasks/apnea.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# :fontawesome-regular-face-surprise: Sleep Apnea Task
-
-## Overview
-
-Sleep apnea detection is the process of identifying hypopnea/apnea events. This task is useful for identifying sleep disorders and for monitoring sleep quality.
-
-## Sleep Apnea Types
-
-There are three main types of sleep apnea: obstructive, central, and mixed. Obstructive sleep apnea (OSA) is the most common type of sleep apnea. It occurs when the throat muscles relax and block the airway during sleep. Central sleep apnea (CSA) occurs when the brain fails to send the proper signals to the muscles that control breathing. Mixed sleep apnea is a combination of both obstructive and central sleep apnea. Hypopnea is a partial blockage of the airway that results in shallow breathing.
-
-=== "Obstructive Sleep Apnea"
-
-    Obstructive sleep apnea (OSA) is the most common type of sleep apnea. It occurs when the throat muscles relax and block the airway during sleep. This leads to pauses in breathing and can result in low oxygen levels in the blood. OSA is often associated with snoring and daytime sleepiness.
-
-=== "Central Sleep Apnea"
-
-    Central sleep apnea (CSA) occurs when the brain fails to send the proper signals to the muscles that control breathing. This results in pauses in breathing during sleep. CSA is less common than OSA and is often associated with heart failure or stroke. Clinically, central sleep apnea is defined as a lack of respiratory effort for at least 10 seconds.
-
-=== "Mixed Sleep Apnea"
-
-    Mixed sleep apnea is a combination of both obstructive and central sleep apnea. It occurs when there is a blockage of the airway and a failure of the brain to send the proper signals to the muscles that control breathing. Mixed sleep apnea is less common than OSA or CSA. Clinically, mixed sleep apnea is defined as a combination of obstructive and central apneas.
-
-=== "Hypopnea"
-
-    Hypopnea is a partial blockage of the airway that results in shallow breathing. It is less severe than apnea but can still disrupt sleep and lead to daytime sleepiness. Clinically, hypopnea is defined as a reduction in airflow of at least 30% for at least 10 seconds, accompanied by a decrease in oxygen saturation of at least 3%.
-
-
-## Model Zoo
-
-The following table provides the latest performance and accuracy results for pre-trained models. Additional result details can be found in [Model Zoo → Apnea](../zoo/apnea.md).
-
-
---8<-- "assets/zoo/apnea/apnea-model-zoo-table.md"
-
----
-
-## Target Classes
-
-Below outlines the classes available for apnea classification. When training a model, the number of classes, mapping, and names must be provided.
-
---8<-- "assets/tasks/apnea/apnea-classes.md"
-
-## References
-
-* [ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing](https://doi.org/10.48550/arXiv.2306.10863)
-* [Multimodal Sleep Apnea Detection with Missing or Noisy Modalities](https://arxiv.org/pdf/2402.17788v1.pdf)
diff --git a/docs/tasks/arousal.md b/docs/tasks/arousal.md
deleted file mode 100644
index 471bfbc..0000000
--- a/docs/tasks/arousal.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Sleep arousal
-
-Sleep arousal is not a registered sleepKIT task. This URL is retained for existing links; no arousal model or workflow is provided here.
-
-[View supported tasks](index.md){ .md-button }
diff --git a/docs/tasks/detect.md b/docs/tasks/detect.md
deleted file mode 100644
index 8b8d8de..0000000
--- a/docs/tasks/detect.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# :material-sleep: Sleep Detection Task
-
-## Overview
-
-The objective of sleep detection is to identify periods of sustained sleep over the course of several days or weeks. The de facto standard for long-term, ambulatory sleep detection is actigraphy, which is a method of monitoring gross motor activity using an accelerometer. However, actigraphy is not a reliable method for sleep detection as it employs very simple heuristics to determine sleep. Often actigraphy can misclassify periods of inactivity or even not-worn as detected sleep.
-
-<!-- <div class="sk-plotly-graph-div">
---8<-- "assets/sleep-detect-demo.html"
-</div> -->
-
-In this task, we look to leverage a light-weight model that can outperform actigraphy similarly using only data from an IMU. For more advanced sleep analysis, refer to the [Sleep Stage Classification](./stage.md). By leveraring Ambiq's ultra-low-power microcontroller along with an ultra-low-power IMU, an efficient AI enabled actigraphy or fitness band will be able to run for weeks off a single charge.
-
-<figure markdown>
-  ![Wrist-based Sleep Classification](../assets/tasks/detect/ambiq-watch.webp){ width="540" }
-  <figcaption>Wrist-based Sleep Detection</figcaption>
-</figure>
-
----
-
-## Model Zoo
-
-The following table provides the latest performance and accuracy results for pre-trained models. Additional result details can be found in [Model Zoo → Detect](../zoo/detect.md).
-
-
---8<-- "assets/zoo/detect/detect-model-zoo-table.md"
-
----
-
-## Target Classes
-
-Below outlines the classes available for sleep detect classification. When training a model, the number of classes, mapping, and names must be provided.
-
---8<-- "assets/tasks/detect/detect-default-class-table.md"
-
-!!! example "Class Mapping"
-
-    Below is an example of a class mapping for a 2-class sleep detect model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped.
-
-    ```json
-    {
-        "num_classes": 2,
-        "class_names": ["AWAKE", "SLEEP"],
-        "class_map": {
-            "0": 0,
-            "1": 1
-        }
-    }
-    ```
-
----
-
-## References
-
-* [AI-Driven sleep staging from actigraphy and heart rate](https://doi.org/10.1371/journal.pone.0285703)
diff --git a/docs/tasks/stage.md b/docs/tasks/stage.md
deleted file mode 100644
index 496c9b0..0000000
--- a/docs/tasks/stage.md
+++ /dev/null
@@ -1,156 +0,0 @@
-# :material-sleep: Sleep Stage Task
-
-## Work with saved features
-
-For a saved-feature workflow, start with the [staging baseline](../staging-baseline.md), which describes input records, preprocessing and checkpoint evaluation. Continue to [staging training](../staging-training.md) to train a model with an explicit split and configuration.
-
-## Overview
-
-The objective of sleep stage assessment is to determine the different stages of sleep (N1, N2, N3, REM, and wake) over the course of a night. The de facto standard for sleep stage assessment is polysomnography (PSG) that requires collecting numerous multi-site physiological signals and a trained sleep technician to perform the assessment. The technician uses the physiological signals to determine the sleep stage based on the American Academy of Sleep Medicine (AASM) guidelines. The identification of stages is possible due to specific electrophysiological signatures present and recorded by EEG, EOG, and EMG signals.
-
-<figure markdown>
-  ![Sleep Stage](../assets/tasks/stage/sleep-stage.svg){ width="640" }
-  <figcaption>Sleep Stage Assessment</figcaption>
-</figure>
-
-
-The focus of this project is to develop a model that can perform sleep stage assessment using only a small subset of the signals that can be captured from same body location (e.g. wrist) that is viable for long-term use. In particular, the current models are trained on physiological signals that can be captured from the wrist for fitness band and smartwatch applications. Traditionally, wrist-based sleep classification has been performed using actigraphy, which is a method of monitoring gross motor activity using an accelerometer. However, actigraphy is not a reliable method for sleep stage assessment. The current model is designed to perform sleep stage assessment using a combination of motor activity, cardiovascular signals (e.g. heart rate), and derived resipiratory signals (e.g. respiration rate).
-
-<figure markdown>
-  ![Wrist-based Sleep Classification](../assets/tasks/stage/ambiq-watch.webp){ width="540" }
-  <figcaption>Wrist-based Sleep Classification</figcaption>
-</figure>
-
----
-
-## Sleep Stages
-
-Sleep stages consists of wake (W), non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. NREM sleep is further divided into three stages: N1, N2, and N3. N1 and N2 are considered light sleep, while N3 is considered deep sleep. REM sleep is considered the dreaming stage of sleep. During the course of a night, a subject will undergo multiple rounds of sleep cycles, each composed of the individual stages. The duration of each cycle and length of each stage various throughout the night. Each sleep cycle typically lasts around 90 to 120 minutes.
-
-<figure class="sleep-stage-chart">
-<div class="sleep-stage-donut" role="img" aria-label="Sleep stage proportions: N1 5 percent, N2 55 percent, N3 10 percent, REM 30 percent."></div>
-<div class="sleep-stage-legend">
-
-| Stage | Share |
-| --- | ---: |
-| N1 | 5% |
-| N2 | 55% |
-| N3 | 10% |
-| REM | 30% |
-
-</div>
-<figcaption>Example sleep-stage proportions. The distribution varies between nights and individuals.</figcaption>
-</figure>
-
-=== "Wake (W)"
-
-    This is the time before or after bed when individual is awake.
-
-    Key Characteristics
-
-    * Alpha waves on the EEG
-    * No K-complexes or sleep spindles
-    * Eye movements are normal
-    * Respiratory rate and heart rate are normal
-    * Body movements are common
-
-=== "Light Sleep (N1)"
-
-    This is the shortest and lightest stage of sleep. It is the transition from wakefulness to sleep. It is characterized by slow eye movements, and alpha and theta waves on the EEG. It is also known as somnolence, or drowsy sleep. This stage lasts around 1 to 5 minutes, consisting of 5% of total sleep time. This stage is extremely difficult by EEG alone, and is often misclassified as wake or N2.
-
-    Key Characteristics:
-
-    * Alpha and theta waves on the EEG
-    * No K-complexes or sleep spindles
-    * Slow eye movements
-    * Respiratory rate and heart rate are normal
-    * Body temperature begins to drop
-    * Muscles relax; might jerk
-
-=== "Core Sleep (N2)"
-
-    This stage represents deeper sleep as the body begins to prepare for deep sleep. Physiologically, the body temperature begins to drop and the heart rate begins to slow. The stage is characterized by sleep spindles and K-complexes on the EEG. Stage 2 sleep lasts around 10 to 25 minutes, consisting of 45% to 55% of total sleep time. The duration increases as the night progresses.
-
-    Key Characteristics:
-
-    * Sleep spindles and K-complexes on the EEG
-    * No eye movements
-    * Respiratory rate and heart rate slows
-    * Body temperature drops
-    * Body movements are rare
-
-=== "Deep Sleep (N3)"
-
-    Stage 3 sleep is the deepest stage of sleep. It is characterized by delta waves on the EEG. It is also known as slow-wave sleep (SWS) or delta sleep. It is difficult to wake someone from this stage. Stage 3 sleep lasts around 20 to 40 minutes, consisting of 3% to 8% of total sleep time. The duration decreases as the night progresses.
-
-    Key Characteristics:
-
-    * Delta waves on the EEG
-    * No eye movements
-    * Blood pressure drops, flow increases
-    * Respiratory rate and heart rate are at lowest levels
-    * Body movements are rare (sleep walking, sleep talking, bed wetting, and night terrors may occur)
-
-=== "REM Sleep (REM)"
-
-    The REM stage is characterized by rapid eye movements, low muscle tone, and dreaming. It is also known as paradoxical sleep. It is difficult to wake someone from this stage. REM sleep lasts around 10 to 60 minutes, consisting of 20% to 25% of total sleep time. The duration increases as the night progresses.
-
-    * Brain activity mimicks normal awake
-    * Rapid eye movements
-    * Respiration increases and irregular
-    * Heart rate increases
-    * Temperature regulation is off
-    * Body becomes immobile
-
----
-
-## Model Zoo
-
-The following table provides the latest performance and accuracy results for pre-trained models. Additional result details can be found in [Model Zoo → Stage](../zoo/stage.md).
-
-
---8<-- "assets/zoo/stage/stage-model-zoo-table.md"
-
----
-
-## Target Classes
-
-Below outlines the classes available for sleep stage classification. When training a model, the number of classes, mapping, and names must be provided.
-
---8<-- "assets/tasks/stage/stage-default-class-table.md"
-
-!!! example "Class Mapping"
-
-    Below is an example of a class mapping for a 4-class sleep stage model. The class map keys are the original class labels and the values are the new class labels. Any class not included will be skipped.
-
-    ```json
-    {
-        "num_classes": 4,
-        "class_names": ["AWAKE", "LIGHT", "DEEP", "REM"],
-        "class_map": {
-            "0": 0,
-            "1": 1,
-            "2": 1,
-            "3": 2,
-            "4": 2,
-            "5": 3
-        }
-    }
-    ```
-
----
-
-## Limitations
-
-Inferring sleep stage without electrophysiological signals is an extremely challenging task. Especially when considering a highly noisy ambulatory environment. Furthermore, the limited time spent in certain sleep stages such as N1 makes it difficult to accurately classify these stages. This is highlighted by the limited interrater reliability of sleep stage scoring. A recent paper by [Kapur et al., 2021](https://doi.org/10.5664/jcsm.9538) performed a meta-analysis of 101 studies and found that the interrater reliability for stage N1 sleep was only fair, while the interrater reliabilities for stage N2 and N3 sleep were moderate. The paper found the Cohen’s kappa for manual, overall sleep scoring was 0.76, indicating substantial agreement (95% confidence interval, 0.71–0.81; P < .001). By sleep stage, the figures were 0.70, 0.24, 0.57, 0.57, and 0.69 for the W, N1, N2, N3, and R stages, respectively. The interrater reliabilities for stage N2 and N3 sleep were moderate, and that for stage N1 sleep was only fair. Taking this into account, we should not expect to achieve perfect sleep stage classification.
-
-
-## References
-
-* [U-Sleep: Resilient High-Frequency Sleep Staging](https://doi.org/10.1038/s41746-021-00440-5)
-* [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://doi.org/10.48550/arXiv.1910.11162)
-* [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://doi.org/10.48550/arXiv.1703.04046)
-* [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://doi.org/10.48550/arXiv.2210.02186)
-* [The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring](https://doi.org/10.3390/s21134302)
-* [Interrater reliability of sleep stage scoring: a meta-analysis](https://doi.org/10.5664/jcsm.9538)
-* [Development of generalizable automatic sleep staging using heart rate and movement based on large databases](https://doi.org/10.1007/s13534-023-00288-6)
diff --git a/docs/usage/cli.md b/docs/usage/cli.md
deleted file mode 100644
index cceb11f..0000000
--- a/docs/usage/cli.md
+++ /dev/null
@@ -1,164 +0,0 @@
-# :octicons-terminal-24: sleepKIT CLI
-
-<div class="termy">
-
-```console
-$ sleepkit --help
-
-sleepKIT CLI Options:
-    --task [detect, stage, apnea]
-    --mode [download, feature, train, evaluate, export, demo]
-    --config ["./path/to/config.json", or '{"raw: "json"}']
-```
-
-</div>
-
-The sleepKIT command line interface (CLI) makes it easy to run a variety of single-line commands without the need for writing any code. You can run all tasks and modes from the terminal with the `sleepkit` command.
-
-!!! example
-
-    === "Syntax"
-        `sleepkit` commands use the following syntax:
-
-        ```bash
-        sleepkit --mode [MODE] --task [TASK] --config [CONFIG]
-        ```
-
-        Or using short flags:
-
-        ```bash
-        sleepkit -m [MODE] -t [TASK] -c [CONFIG]
-        ```
-
-        Where:
-
-        * `MODE` is one of `download`, `feature`, `train`, `evaluate`, `export`, or `demo`
-        * `TASK` is one of `detect`, `stage`, or `apnea`
-        * `CONFIG` is configuration as JSON content or file path
-
-    === "Download"
-        Download datasets specified in the configuration file.
-
-        ```bash
-        sleepkit -t stage -m download -c ./configuration.json
-        ```
-    === "Feature"
-        Extract features from the datasets using the supplied configuration file.
-
-        ```bash
-        sleepkit -t stage -m feature -c ./configuration.json
-        ```
-
-    === "Train"
-        Train a task model using the supplied configuration file.
-
-        ```bash
-        sleepkit -t stage -m train -c ./configuration.json
-        ```
-
-    === "Evaluate"
-        Evaluate the trained task model using the supplied configuration file.
-
-        ```bash
-        sleepkit -t stage -m evaluate  -c ./configuration.json
-        ```
-
-    === "Demo"
-        Run demo on trained task model using the supplied configuration file.
-
-        ```bash
-        sleepkit -t stage -m demo -c ./configuration.json
-        ```
-
-
-!!! Note "Configuration File"
-
-    The configuration file is a JSON file that contains all the necessary parameters for the task. The configuration file can be passed as a file path or as a JSON string. In addition, a single configuration file can be used for all `modes`- only needed parameters will be extracted for the given `mode` running.  Please refer to the [Configuration](../modes/configuration.md) section for more details.
-
----
-
-## [Download](../modes/download.md)
-
-The `download` command is used to download all datasets specified in the configuration file. Please refer to [Datasets](../datasets/index.md) for details on the available datasets.
-
-
-!!! Example "CLI"
-
-    The following command will download and prepare all datasets specified in configuration JSON file.
-
-    ```bash
-    sleepkit --task stage --mode download --config ./configuration.json
-    ```
-
----
-
-## [Feature](../features/index.md)
-
-The `feature` command is used to extract features from the datasets specified in the configuration file using the specified feature set generator. Additional parameters can be set in the configuration file. Please refer to [FeatureParams](../modes/configuration.md#featureparams) and [TaskParams](../modes/configuration.md#taskparams) to see supported options.
-
-!!! Example "CLI"
-
-    The following command will extract features from the datasets using the reference configuration:
-
-    ```bash
-    sleepkit --task stage --mode feature --config ./configuration.json
-    ```
-
----
-
-## [Train](../modes/train.md)
-
-The `train` command is used to train a sleepKIT model for the specified `task` and `feature set`. Each task provides a reference routine for training the model. The routine can be customized via the configuration file. Please refer to [TaskParams](../modes/configuration.md#taskparams) to see supported options.
-
-!!! Example "CLI"
-
-    The following command will train a task model using the reference configuration:
-
-    ```bash
-    sleepkit --task stage --mode train --config ./configuration.json
-    ```
-
----
-
-## [Evaluate](../modes/evaluate.md)
-
-The `evaluate` command will test the performance of the model on the reserved test sets for the specified `task`. The routine can be customized via the configuration file. Please refer to [TaskParams](../modes/configuration.md#taskparams) to see supported options.
-
-!!! example "CLI"
-
-    The following command will test the task model using the reference configuration:
-
-    ```bash
-    sleepkit --task stage --mode evaluate --config ./configuration.json
-    ```
-
----
-
-## [Export](../modes/export.md)
-
-The `export` command will convert the trained TensorFlow model into both TensorFlow Lite (TFL) and TensorFlow Lite for micro-controller (TFLM) variants. The command will also verify the models' outputs match. The activations and weights can be quantized by configuring the `quantization` section in the configuration file. Once converted, the TFLM header file will be copied to location specified by `tflm_file`. Please refer to [TaskParams](../modes/configuration.md#taskparams) to see supported options.
-
-!!! example "CLI"
-
-    The following command will export the task model to TF Lite and TFLM:
-
-    ```bash
-    sleepkit --task stage --mode export --config ./configuration.json
-    ```
-
----
-
-## [Demo](../modes/demo.md)
-
-
-The `demo` command is used to run a task-level demonstration using either the PC or EVB as backend inference engine. Please refer to [TaskParams](../modes/configuration.md#taskparams) to see supported options.
-
-!!! Example "CLI"
-
-    The following command will run a demo on the trained task model using the same supplied configuration file.
-
-    ```bash
-    sleepkit --task stage --mode demo --config ./configuration.json
-    ```
-
----
diff --git a/docs/usage/python.md b/docs/usage/python.md
deleted file mode 100644
index 2f2e84c..0000000
--- a/docs/usage/python.md
+++ /dev/null
@@ -1,98 +0,0 @@
-# :simple-python: Python Usage
-
-__sleepKIT__ python package allows for more fine-grained control and customization. You can use the package to train, evaluate, and deploy models for both built-in tasks and custom tasks. In addition, custom datasets and model architectures can be created and registered with corresponding factories.
-
-## Overview
-
-The main components of sleepKIT include the following:
-
-### [Tasks](../tasks/index.md)
-
-A [Task](../tasks/index.md) inherits from the [sk.Task](/sleepkit/api/sleepkit/tasks/task) class and provides implementations for each of the main modes: download, feature, train, evaluate, export, and demo. Each mode is provided with a set of parameters defined by [sk.TaskParams](/sleepkit/api/sleepkit/defines). Additional task-specific parameters can be extended to the `TaskParams` class. These tasks are then registered and accessed via the `sk.TaskFactory` using a unique task name as the key and the custom Task class as the value.
-
-```py linenums="1"
-import sleepkit as sk
-
-task = sk.TaskFactory.get('stage')
-```
-
-### [Datasets](../datasets/index.md)
-
-A dataset inherits from the [sk.Dataset](/sleepkit/api/sleepkit/datasets/dataset) class and provides implementations for downloading, preparing, and loading the dataset. Each dataset is provided with a set of custom parameters for initialization. The datasets are registered and accessed via the [DatasetFactory](/sleepkit/api/sleepkit/datasets/factory) using a unique dataset name as the key and the Dataset class as the value.
-
-```py linenums="1"
-import sleepkit as sk
-
-ds = sk.DatasetFactory.get('cmidss')(path='./datasets/cmidss')
-```
-
-### [Features](../features/index.md)
-
-Since each task will require specific transformations of the data, a feature store is used to generate features from the dataset. The feature store provides a set of feature sets that can be used by the task. Each feature set is provided with a set of custom parameters for initialization. The feature sets are registered and accessed via the [sk.FeatureFactory](/sleepkit/api/sleepkit/features/factory) using a unique feature set name as the key and the Feature class as the value.
-
-
-
-### [Models](../models/index.md)
-
-Lastly, sleepKIT leverages [helia-edge's](https://ambiqai.github.io/helia-edge/) customizable model architectures. To enable creating custom network topologies from configuration files, sleepKIT provides a `sk.ModelFactory` that allows you to create models by specifying the model key and the model parameters. Each item in the factory is a callable that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`.
-
-```python
-import keras
-import sleepkit as sk
-
-inputs = keras.Input((256, 1), dtype="float32")
-num_classes = 4
-model_params = {"blocks": [{"filters": 16, "kernel": [1, 3]}]}
-
-model = sk.ModelFactory.get('tcn')(
-    inputs=inputs,
-    params=model_params,
-    num_classes=num_classes
-)
-
-```
-
-## Usage
-
-### Running a built-in task w/ existing datasets
-
-1. Create a task configuration file defining the model, datasets, class labels, mode parameters, and so on. Have a look at the [sk.TaskParams](../modes/configuration.md#taskparams) for more details on the available parameters.
-
-2. Leverage `sk.TaskFactory` to get the desired built-in task.
-
-3. Run the task's main modes: `download`, `feature`, `train`, `evaluate`, `export`, and/or `demo`.
-
-
-```py linenums="1"
-
-from pathlib import Path
-import sleepkit as sk
-
-params = sk.TaskParams.model_validate_json(
-    Path("configuration.json").read_text()
-)
-
-task = sk.TaskFactory.get("stage")
-
-task.download(params)  # Download dataset(s)
-
-task.feature(params)  # Generate features
-
-task.train(params)  # Train the model
-
-task.evaluate(params)  # Evaluate the model
-
-task.export(params)  # Export to TFLite
-
-```
-
-**Example configuration**
---8<-- "assets/usage/json-configuration.md"
-
-### Running a custom task w/ custom datasets
-
-To create a custom task, check out the [Bring-Your-Own-Task Guide](../tasks/byot.md).
-
-To create a custom dataset, check out the [Bring-Your-Own-Dataset Guide](../datasets/byod.md).
-
----
diff --git a/docs/zoo/apnea.md b/docs/zoo/apnea.md
deleted file mode 100644
index f6be7d6..0000000
--- a/docs/zoo/apnea.md
+++ /dev/null
@@ -1,71 +0,0 @@
-# Sleep Apnea Detection
-
-## Model Overview
-
-The following table provides the latest pre-trained models for sleep apnea. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
-
---8<-- "assets/zoo/apnea/apnea-model-zoo-table.md"
-
-## Model Details
-
-=== "SA-2-TCN-SM"
-
-    The __SA-2-TCN-SM__ model is a 2-stage sleep apnea detection model that uses a Temporal convolutional network (TCN). The model is trained on PPG sensor data collected from the wrist and is able to locate apnea/hypopnea events.
-
-    - **Location**: Wrist
-    - **Classes**: Normal, Apnea
-    - **Frame Size**: 10 minutes
-    - **Datasets**: [MESA](../datasets/mesa.md)
-    - **Features**: [FS-W-P-5](../features/fs_w_p_5.md)
-
-## Model Performance
-
-=== "SA-2-TCN-SM"
-
-    The following plots show the model's performance in detecting apnea/hypopnea events. The first plot shows the confusion matrix for apnea detection.
-
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/apnea/sa-2-tcn-sm-cm.html"
-    </div>
-
-    The following plot shows the model's ability to detect AHI (Apnea-Hypopnea Index) compared to the ground truth AHI values. The x-axis represents the true AHI values, while the y-axis represents the predicted AHI values. The plot shows a strong correlation between the true and predicted AHI values.
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/apnea/sa-2-tcn-sm-ahi-scatter.html"
-    </div>
-
-    The following table provides the corresponding confusion matrix for AHI.
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/apnea/sa-2-tcn-sm-ahi-cm.html"
-    </div>
-
----
-
-## EVB Performance
-
-
-The following table provides the latest performance results when running on Apollo4 Plus EVB. These results are obtained using neuralSPOTs [Autodeploy tool](https://ambiqai.github.io/neuralSPOT/docs/From%20TF%20to%20EVB%20-%20testing%2C%20profiling%2C%20and%20deploying%20AI%20models.html). From neuralSPOT repo, the following command can be used to capture EVB results via Autodeploy:
-
-```console
-python -m ns_autodeploy \
---tflite-filename model.tflite \
---model-name model \
---cpu-mode 192 \
---arena-size-scratch-buffer-padding 0 \
---max-arena-size 60 \
-```
-
---8<-- "assets/zoo/apnea/apnea-model-hw-table.md"
-
-## Downloads
-
-=== "SA-2-TCN-SM"
-
-    | Asset                                                                | Description                   |
-    | -------------------------------------------------------------------- | ----------------------------- |
-    | [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/configuration.json)   | Configuration file            |
-    | [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/model.keras)            | Keras Model file              |
-    | [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/model.tflite)       | TFLite Model file             |
-    | [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/apnea/sa-2-tcn-sm/latest/metrics.json)       | Metrics file                  |
diff --git a/docs/zoo/detect.md b/docs/zoo/detect.md
deleted file mode 100644
index 3e6c3ca..0000000
--- a/docs/zoo/detect.md
+++ /dev/null
@@ -1,79 +0,0 @@
-# Sleep Detection Model
-
-## Model Overview
-
-The following table provides the latest pre-trained models for sleep detection. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
-
---8<-- "assets/zoo/detect/detect-model-zoo-table.md"
-
----
-
-## Model Details
-
-=== "SD-2-TCN-SM"
-
-    The __SD-2-TCN-SM__ model is a 2-stage sleep detection model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on accelerometer sensor data collected from the wrist and is able to classify sleep and wake stages.
-
-    - **Location**: Wrist
-    - **Classes**: Awake, Sleep
-    - **Frame Size**: 2 hours
-    - **Datasets**: [MESA](../datasets/mesa.md)
-    - **Features**: [FS-W-A-5](../features/fs_w_a_5.md)
-
-## Model Performance
-
-=== "SD-2-TCN-SM"
-
-    ### Confusion Matrix
-
-    The confusion matrix provides a detailed breakdown of the model's performance on the test set. The rows represent the true labels, while the columns represent the predicted labels. The diagonal elements represent the number of correct predictions for each class, while the off-diagonal elements represent the number of incorrect predictions.
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/detect/detect-2-cm.html"
-    </div>
-
-    ### Sleep Efficiency Plot
-
-    The following plot shows the model's performance in detecting sleep efficieny- percentage of time spent asleep while in bed. The x-axis represents the true sleep efficiency, while the y-axis represents the predicted sleep efficiency.
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/detect/detect-2-eff.html"
-    </div>
-
-    ### Total Sleep Time (TST) Plot
-
-    The following plot shows the model's performance in detecting total sleep time (TST). The x-axis represents the true TST, while the y-axis represents the predicted TST. The plot shows a strong correlation between the true and predicted TST values.
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/detect/detect-2-tst.html"
-    </div>
-
----
-
-## EVB Performance
-
-
-The following table provides the latest performance results when running on Apollo4 Plus EVB. These results are obtained using neuralSPOTs [Autodeploy tool](https://ambiqai.github.io/neuralSPOT/docs/From%20TF%20to%20EVB%20-%20testing%2C%20profiling%2C%20and%20deploying%20AI%20models.html). From neuralSPOT repo, the following command can be used to capture EVB results via Autodeploy:
-
-```console
-python -m ns_autodeploy \
---tflite-filename model.tflite \
---model-name model \
---cpu-mode 192 \
---arena-size-scratch-buffer-padding 0 \
---max-arena-size 60 \
-```
-
---8<-- "assets/zoo/detect/detect-model-hw-table.md"
-
-
-## Downloads
-
-=== "SD-2-TCN-SM"
-
-    | Asset                                                                | Description                   |
-    | -------------------------------------------------------------------- | ----------------------------- |
-    | [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/configuration.json)   | Configuration file            |
-    | [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/model.keras)            | Keras Model file              |
-    | [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/model.tflite)       | TFLite Model file             |
-    | [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/detect/sd-2-tcn-sm/latest/metrics.json)       | Metrics file                  |
diff --git a/docs/zoo/index.md b/docs/zoo/index.md
deleted file mode 100644
index eb40344..0000000
--- a/docs/zoo/index.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# :material-graph: Model Zoo
-
-A number of pre-trained models are available for download to use in your own project. These models are trained on the datasets listed below and are available in TensorFlow flatbuffer formats.
-
-Model licenses are specific to each release and its data sources. Public downloads
-do not imply commercial-use permission or a BSD license for weights. See the
-[licensing policy](../model-licensing-policy.md); historical models have not yet
-received a complete per-artifact provenance and licensing review.
-
-## [Sleep Detect](./detect.md)
-
-The following table provides the latest performance and accuracy results for sleep detection models.
-
---8<-- "assets/zoo/detect/detect-model-zoo-table.md"
-
-
-## [Sleep Stage](./stage.md)
-
-The following table provides the latest performance and accuracy results for sleep staging models.
-
---8<-- "assets/zoo/stage/stage-model-zoo-table.md"
-
-## [Sleep Apnea](./apnea.md)
-
-The following table provides the latest performance and accuracy results for sleep apnea detection models.
-
---8<-- "assets/zoo/apnea/apnea-model-zoo-table.md"
-
-<!-- ## Sleep Arousal
-
-The following table provides the latest performance and accuracy results for sleep arousal classification models. Additional result details can be found in [Zoo → Arousal](./arousal.md).
-
---8<-- "assets/zoo/arousal/arousal-model-zoo-table.md" -->
diff --git a/docs/zoo/stage.md b/docs/zoo/stage.md
deleted file mode 100644
index 16ff8e8..0000000
--- a/docs/zoo/stage.md
+++ /dev/null
@@ -1,222 +0,0 @@
-# Sleep Stage Models
-
-## Model Overview
-
-The following table provides the latest pre-trained models for sleep detection. Below we also provide additional details including training configuration, accuracy metrics, and hardware performance results for the models.
-
---8<-- "assets/zoo/stage/stage-model-zoo-table.md"
-
-## Model Details
-
-=== "SS-2-TCN-SM"
-
-    The __SS-2-TCN-SM__ model is a 2-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify sleep and wake stages. The model requires only 10K parameters and achieves an accuracy of 88.8% and an average AP score of 96.2%.
-
-    - **Location**: Wrist
-    - **Classes**: Awake, Sleep
-    - **Frame Size**: 2 hours
-    - **Datasets**: [MESA](../datasets/mesa.md)
-    - **Features**: [FS-W-PA-14](../features/fs_w_pa_14.md)
-
-    | Base Class              | Target Class | Label     |
-    | ----------------------- | ------------ | --------- |
-    | 0-WAKE                  | 0            | WAKE      |
-    | 1-N1, 2-N2, 3-N3, 5-REM | 1            | SLEEP     |
-
-=== "SS-3-TCN-SM"
-
-    The __SS-3-TCN-SM__ model is a 3-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify awake, sleep and rem stages. The model requires only 14K parameters and achieves an accuracy of 84% and an average AP score of 91.5%.
-
-    - **Location**: Wrist
-    - **Classes**: Awake, NREM, REM
-    - **Frame Size**: 2 hours
-    - **Datasets**: [MESA](../datasets/mesa.md)
-    - **Features**: [FS-W-PA-14](../features/fs_w_pa_14.md)
-
-    | Base Class              | Target Class | Label     |
-    | ----------------------- | ------------ | --------- |
-    | 0-WAKE                  | 0            | WAKE      |
-    | 1-N1, 2-N2, 3-N3        | 1            | NREM      |
-    | 5-REM                   | 2            | REM       |
-
-=== "SS-4-TCN-SM"
-
-    The __SS-4-TCN-SM__ model is a 4-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify awake, core, deep, and rem stages. The model requires only 18K parameters and achieves an accuracy of 75.8% and an average AP score of 83.1%.
-
-    - **Location**: Wrist
-    - **Classes**: Awake, Core, Deep, REM
-    - **Frame Size**: 2 hours
-    - **Datasets**: [MESA](../datasets/mesa.md)
-    - **Features**: [FS-W-PA-14](../features/fs_w_pa_14.md)
-
-    | Base Class              | Target Class | Label     |
-    | ----------------------- | ------------ | --------- |
-    | 0-WAKE                  | 0            | WAKE      |
-    | 1-N1, 2-N2              | 1            | CORE      |
-    | 3-N3                    | 2            | DEEP      |
-    | 5-REM                   | 3            | REM       |
-
-
-=== "SS-5-TCN-SM"
-
-    The __SS-5-TCN-SM__ model is a 5-stage sleep stage model that uses a Temporal Convolutional Network (TCN) architecture. The model is trained on PPG and IMU data collected from the wrist and is able to classify awake, n1, n2, n3, and rem stages. The model requires only 17K parameters and achieves an accuracy of 70.4% and an average AP score of 76.4%.
-
-    - **Location**: Wrist
-    - **Classes**: Awake, N1, N2, N3, REM
-    - **Frame Size**: 2 hours
-    - **Datasets**: [MESA](../datasets/mesa.md)
-    - **Features**: [FS-W-PA-14](../features/fs_w_pa_14.md)
-
-    | Base Class              | Target Class | Label     |
-    | ----------------------- | ------------ | --------- |
-    | 0-WAKE                  | 0            | WAKE      |
-    | 1-N1                    | 1            | N1        |
-    | 2-N2                    | 2            | N2        |
-    | 3-N3                    | 3            | N3        |
-    | 5-REM                   | 4            | REM       |
-
----
-
-## Model Performance
-
-=== "SS-2-TCN-SM"
-
-    ### Confusion Matrix
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/stage/ss-2-tcn-sm-cm.html"
-    </div>
-
-    ### Sleep Efficiency Plot
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/stage/ss-2-tcn-sm-eff.html"
-    </div>
-
-    ### Total Sleep Time (TST) Plot
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/stage/ss-2-tcn-sm-tst.html"
-    </div>
-
-=== "SS-3-TCN-SM"
-
-    ### Confusion Matrix
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/stage/ss-3-tcn-sm-cm.html"
-    </div>
-
-
-=== "SS-4-TCN-SM"
-
-    ### Confusion Matrix
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/stage/ss-4-tcn-sm-cm.html"
-    </div>
-
-=== "SS-5-TCN-SM"
-
-    ### Confusion Matrix
-
-    <div class="sk-plotly-graph-div">
-    --8<-- "assets/zoo/stage/ss-5-tcn-sm-cm.html"
-    </div>
-
----
-
-## EVB Performance
-
-The following table provides the latest performance and accuracy results of all models when running on Apollo4 Plus EVB. These results are obtained using neuralSPOTs [Autodeploy tool](https://ambiqai.github.io/neuralSPOT/docs/From%20TF%20to%20EVB%20-%20testing%2C%20profiling%2C%20and%20deploying%20AI%20models.html). From neuralSPOT repo, the following command can be used to capture EVB results via Autodeploy:
-
-``` console
-python -m ns_autodeploy \
---tflite-filename model.tflite \
---model-name model \
---cpu-mode 192 \
---arena-size-scratch-buffer-padding 0 \
---max-arena-size 80 \
-
-```
-
---8<-- "assets/zoo/stage/stage-model-hw-table.md"
-
-In addition, we can capture statistics from each layer. The following bar plot provides the latency of each block in the 4-stage sleep classification TCN model. For example, `ENC` refers to initial encoder 1-d seperable convulional layer, `B1.1` refers to all the layers in block 1, depth 1, `B1.2` refers to block 1, depth 2, and so on. We can see that as we go deeper into the network we see an increase in latency due to the increasing number of channels. The final `DEC` layer refers to the decoder layer which is a 1-d convolutional layer with 3 output channels (4 classes).
-
-<div class="sk-plotly-graph-div">
---8<-- "assets/zoo/stage/block-latency.html"
-</div>
-
----
-
-## Comparison
-
-We compare our 3-stage and 4-stage model to the SLAMSS model from [Song et al., 2023](https://doi.org/10.1371/journal.pone.0285703). Their model was also trained on MESA dataset using only motor and cardiac physiological signals. In particular, they extract activity count, heart rate, and heart rate standard deviation in 30 second epochs. They fed 12 epochs (6 minutes) of the 3 features (12x6) as input to the network. The newtork consists of 3 1-D CNN layers, 2 LSTM layers, and 1 attention layer. The underlying design of the attention block is unclear but using only the 3 CNN and 2 LSTM layers the network requires roughly 8.8 MFLOPS per epoch. This equates to roughly __450X__ more computation (__1,056 MFLOPS/hr__) compared to our 4-stage sleep classification model (__2.3 MFLOPS/hr__).
-
-<!-- SLAMSS FLOPS:
-K=9, Cin=3, Cout=64, T=12, P=4, S=1
-CNN1: 3*64*9*12 = 20,736
-CNN2: 64*64*9*12 = 442,368
-CNN3: 64*64*9*12 = 442,368
-LSTM1: 2*((64+256+1)*4*256+256)*12 = 7,895,040
-LSTM2: 2*((64+256+1)*4*256+256)*12 = 75,816
-TOTAL: 20736+442368+442368+7895040+75816 -->
-
-### 3-Stage Sleep Classification (MESA)
-
-| Reference         | Acc       | F1        | WAKE      | NREM      | REM       |
-| ----------------- | --------- | --------- | --------- | --------- | --------- |
-| [Song et al., 2023](https://doi.org/10.1371/journal.pone.0285703) | 79.1      | 80.0      | 78.0      | 81.8      | 70.9      |
-| **sleepKIT**      | **83.9**  | **84.2**  | **80.3**  | **86.6**  | **83.5**  |
-
-
-### 4-Stage Sleep Classification (MESA)
-
-| Reference         | Acc       | F1        | WAKE      | CORE      | DEEP      | REM       |
-| ----------------- | --------- | --------- | --------- | --------- | --------- | --------- |
-| [Song et al., 2023](https://doi.org/10.1371/journal.pone.0285703) | 70.0      | 72.0      | 78.7      | 66.3      | **55.9**      | 63.0      |
-| sleepKIT          | **75.8**  | **76.4**  | **80.6**  | **73.9**  | 52.2  | **81.7**  |
-
-
----
-
-## Downloads
-
-=== "SS-2-TCN-SM"
-
-    | Asset                                                                | Description                   |
-    | -------------------------------------------------------------------- | ----------------------------- |
-    | [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/configuration.json)   | Configuration file            |
-    | [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/model.keras)            | Keras Model file              |
-    | [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/model.tflite)       | TFLite Model file             |
-    | [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-2-tcn-sm/latest/metrics.json)       | Metrics file                  |
-
-=== "SS-3-TCN-SM"
-
-    | Asset                                                                | Description                   |
-    | -------------------------------------------------------------------- | ----------------------------- |
-    | [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/configuration.json)   | Configuration file            |
-    | [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/model.keras)            | Keras Model file              |
-    | [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/model.tflite)       | TFLite Model file             |
-    | [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-3-tcn-sm/latest/metrics.json)       | Metrics file                  |
-
-=== "SS-4-TCN-SM"
-
-    | Asset                                                                | Description                   |
-    | -------------------------------------------------------------------- | ----------------------------- |
-    | [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/configuration.json)   | Configuration file            |
-    | [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/model.keras)            | Keras Model file              |
-    | [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/model.tflite)       | TFLite Model file             |
-    | [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-4-tcn-sm/latest/metrics.json)       | Metrics file                  |
-
-=== "SS-5-TCN-SM"
-
-    | Asset                                                                | Description                   |
-    | -------------------------------------------------------------------- | ----------------------------- |
-    | [configuration.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/configuration.json)   | Configuration file            |
-    | [model.keras](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/model.keras)            | Keras Model file              |
-    | [model.tflite](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/model.tflite)       | TFLite Model file             |
-    | [metrics.json](https://ambiqai-model-zoo.s3.us-west-2.amazonaws.com/sleepkit/stage/ss-5-tcn-sm/latest/metrics.json)       | Metrics file                  |
-
----
diff --git a/mkdocs.yml b/mkdocs.yml
deleted file mode 100644
index 37e3d91..0000000
--- a/mkdocs.yml
+++ /dev/null
@@ -1,227 +0,0 @@
-site_name: sleepKIT
-site_dir: "site"
-site_url: "https://ambiqai.github.io/sleepkit"
-site_description: sleepKIT, perform AI-based sleep monitoring tasks
-repo_name: AmbiqAI/sleepkit
-repo_url: https://github.com/AmbiqAI/sleepkit
-copyright: Copyright &copy; 2023 Ambiq
-
-nav:
-  - Home:
-    - Home: index.md
-    - Quickstart: quickstart.md
-    - Shared foundation: shared-foundation.md
-    - Reusable blocks: reusable-blocks.md
-    - Staging baseline: staging-baseline.md
-    - Staging training: staging-training.md
-    - Golden experiment: detection-golden.md
-    - Detection profiling: detection-profiling.md
-    - Preparation performance: detection-preparation-performance.md
-    - Licensing: model-licensing-policy.md
-    - Draft model terms: licenses/ambiq-device-model-license-draft.md
-    - Modes:
-      - modes/index.md
-      - Download: modes/download.md
-      - Feature: features/index.md
-      - Train: modes/train.md
-      - Evaluate: modes/evaluate.md
-      - Export: modes/export.md
-      - Demo: modes/demo.md
-    - Tasks:
-      - tasks/index.md
-      - Detect: tasks/detect.md
-      - Stage: tasks/stage.md
-      - Apnea: tasks/apnea.md
-      - BYOT: tasks/byot.md
-    - Datasets:
-      - datasets/index.md
-    - Features:
-      - features/index.md
-    - Models:
-      - models/index.md
-    - Model Zoo:
-      - zoo/index.md
-    - Guides:
-      - guides/index.md
-    - API: api/
-
-  - Quickstart:
-    - quickstart.md
-    - Usage:
-      - CLI: usage/cli.md
-      - Python: usage/python.md
-
-  - Tasks:
-    - tasks/index.md
-    - Detect: tasks/detect.md
-    - Stage: tasks/stage.md
-    - Apnea: tasks/apnea.md
-    - BYOT: tasks/byot.md
-
-  - Modes:
-    - modes/index.md
-    - Configuration: modes/configuration.md
-    - Download: modes/download.md
-    - Feature: features/index.md
-    - Train: modes/train.md
-    - Evaluate: modes/evaluate.md
-    - Export: modes/export.md
-    - Demo: modes/demo.md
-
-  - Datasets:
-    - datasets/index.md
-    - CMIDSS: datasets/cmidss.md
-    - MESA: datasets/mesa.md
-    - YSYW: datasets/ysyw.md
-    - STAGES: datasets/stages.md
-    - Synthetic: datasets/synthetic.md
-    - BYOD: datasets/byod.md
-
-  - Features:
-    - features/index.md
-    - FS-W-PA-14: features/fs_w_pa_14.md
-    - FS-C-EAR-9: features/fs_c_ear_9.md
-    - FS-W-A-5: features/fs_w_a_5.md
-    - FS-H-E-10: features/fs_h_e_10.md
-    - FS-W-P-5: features/fs_w_p_5.md
-    - BYOFS: features/byofs.md
-
-  - Models:
-    - models/index.md
-    - BYOM: models/byom.md
-
-  - Model Zoo:
-    - zoo/index.md
-    - Detect: zoo/detect.md
-    - Stage: zoo/stage.md
-    - Apnea: zoo/apnea.md
-
-  - Guides:
-    - guides/index.md
-    - "Sleep Staging Ablation": guides/stage-ablation.md
-    - Train Sleep Detect Model: guides/train-detect-model.ipynb
-
-  - API: api/
-
-theme:
-  name: material
-  favicon: assets/favicon.png
-  logo: assets/logo-white.png
-  icon:
-      repo: fontawesome/brands/github-alt
-  font:
-    text: Roboto
-    code: Roboto Mono
-  palette:
-    - media: "(prefers-color-scheme)"
-      primary: pink
-      accent: indigo
-      toggle:
-        icon: material/brightness-auto
-        name: Use system theme
-    - media: "(prefers-color-scheme: light)"
-      scheme: default
-      primary: pink
-      accent: indigo
-      toggle:
-        icon: material/brightness-7
-        name: Switch to light theme
-    - media: "(prefers-color-scheme: dark)"
-      scheme: slate
-      primary: pink
-      accent: indigo
-      toggle:
-        icon: material/brightness-4
-        name: Switch to dark theme
-  features:
-    - content.code.annotate
-    - content.code.copy
-    - content.tooltips
-    - search.highlight
-    - search.share
-    - search.suggest
-    - toc.follow
-    - navigation.top
-    - navigation.tabs
-    - navigation.tabs.sticky
-    - navigation.prune
-    - navigation.footer
-    - navigation.tracking
-    - navigation.instant
-    - navigation.instant.progress
-    - navigation.indexes
-    - navigation.sections  # navigation.expand or navigation.sections
-    - content.tabs.link # all code tabs change simultaneously
-
-plugins:
-  - search
-  - gen-files:
-      scripts:
-      - scripts/gen_ref_pages.py
-  - literate-nav:
-      nav_file: summary.md
-  - mkdocstrings:
-      handlers:
-        python:
-          import:
-          - https://docs.python.org/3/objects.inv
-          - https://numpy.org/doc/stable/objects.inv
-          options:
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-            docstring_style: google
-            docstring_section_style: list
-            line_length: 120
-            show_root_heading: false
-            heading_level: 2
-            merge_init_into_class: true
-            show_root_full_path: false
-            parameter_headings: true
-            show_symbol_type_heading: true
-            group_by_category: true
-            show_category_heading: true
-            modernize_annotations: true
-            members_order: source
-            filters: ["!^_", "^__init__$"]
-            separate_signature: true
-            show_signature: true
-            show_signature_annotations: true
-            show_docstring_attributes: true
-            show_source: true
-
-  - mkdocs-jupyter:
-      include_requirejs: true
-      include_source: true
-
-extra_css:
-  - css/mkdocstrings.css
-  - css/termynal.css
-  - css/custom.css
-
-extra_javascript:
-  - js/termynal.js
-  - js/custom.js
-  - https://cdn.plot.ly/plotly-2.33.0.min.js
-
-markdown_extensions:
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-  - attr_list
-  - pymdownx.arithmatex:
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-  - pymdownx.details
-  - pymdownx.highlight:
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-  - pymdownx.inlinehilite
-  - pymdownx.snippets:
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-  - pymdownx.superfences:
-      custom_fences:
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-  - pymdownx.tabbed:
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-  - pymdownx.emoji:
-      emoji_index: !!python/name:material.extensions.emoji.twemoji
-      emoji_generator: !!python/name:material.extensions.emoji.to_svg
-  - tables
-  - md_in_html
-  - sane_lists
diff --git a/docs/guides/train-detect-model.ipynb b/notebooks/archive/previous-docs-train-detect-model.ipynb
similarity index 100%
rename from docs/guides/train-detect-model.ipynb
rename to notebooks/archive/previous-docs-train-detect-model.ipynb
diff --git a/pyproject.toml b/pyproject.toml
index 2bb9e88..ccc75f2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -58,18 +58,6 @@ dev = [
   "pydot>=2.0.0,<3.0",
   "ruff>=0.5.1,<1.0",
 ]
-docs = [
-  "mkdocs>=1.5.3,<2.0",
-  "mkdocs-material>=9.5.8,<10.0",
-  "mkdocstrings-python>=1.8.0,<2.0",
-  "markdown-include>=0.8.1,<1.0",
-  "mkdocs-exclude>=1.0.2,<2.0",
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-  "mkdocs-literate-nav>=0.6.1,<1.0",
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 [build-system]
 requires = ["setuptools>=61.0", "wheel"]
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deleted file mode 100644
index ca474b9..0000000
--- a/scripts/gen_ref_pages.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Generate the code reference pages and navigation."""
-
-from pathlib import Path
-
-import mkdocs_gen_files
-
-nav = mkdocs_gen_files.Nav()
-
-root = Path(__file__).parent.parent
-src = root
-project = src / "sleepkit"
-
-for path in sorted(project.rglob("*.py")):
-    module_path = path.relative_to(src).with_suffix("")
-    doc_path = path.relative_to(src).with_suffix(".md")
-    full_doc_path = Path("api", doc_path)
-
-    parts = tuple(module_path.parts)
-
-    if parts[-1] == "__init__":
-        parts = parts[:-1]
-        doc_path = doc_path.with_name("index.md")
-        full_doc_path = full_doc_path.with_name("index.md")
-    elif parts[-1] == "__main__":
-        continue
-
-    nav[parts] = doc_path.as_posix()
-
-    with mkdocs_gen_files.open(full_doc_path, "w") as fd:
-        ident = ".".join(parts)
-        fd.write(f"::: {ident}")
-
-    mkdocs_gen_files.set_edit_path(full_doc_path, path.relative_to(root))
-
-with mkdocs_gen_files.open("api/summary.md", "w") as nav_file:
-    nav_file.writelines(nav.build_literate_nav())
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index 828b407..a3d1657 100644
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 version = "0.2.13"

From 3c68048899fa863bdc0dadcc17d11ec61685e11f Mon Sep 17 00:00:00 2001
From: Adam Page <apage224@gmail.com>
Date: Thu, 1 Oct 2026 08:57:51 -0500
Subject: [PATCH 2/2] docs: resolve canonical content review findings

---
 HANDOFF.md                                           | 12 ++++--------
 astro-site/README.md                                 |  2 +-
 astro-site/scripts/canonical-content.test.mjs        |  2 +-
 astro-site/scripts/check-output.mjs                  | 11 +++++++++++
 astro-site/src/content/docs/modes/download.mdx       |  2 +-
 .../detection-preparation-performance.md             |  4 ++--
 .../archive/previous-docs-train-detect-model.ipynb   |  4 ++--
 notebooks/train-detect-model.ipynb                   |  4 ++--
 8 files changed, 24 insertions(+), 17 deletions(-)

diff --git a/HANDOFF.md b/HANDOFF.md
index 84be044..1256f95 100644
--- a/HANDOFF.md
+++ b/HANDOFF.md
@@ -1,13 +1,9 @@
 # Canonical Astro documentation
 
-Issue: AmbiqAI/sleepkit#48. Branch: codex/canonical-astro-docs.
+Goal: retire the MkDocs compatibility layer while preserving public content and routes.
 
-Goal: retire the MkDocs compatibility layer without changing published content or runtime behavior.
+PR: https://github.com/AmbiqAI/sleepkit/pull/49. Authored Markdown/MDX, navigation, redirects and static assets now belong to Astro. API pages, notebooks and downloads remain generated. MkDocs configuration and unused dependencies are removed.
 
-Baseline build passed. Source and output baselines are saved under /tmp/sleepkit-canonical-baseline. Worktree is isolated from prior migration work.
+Review: notebook source links, download command examples and generation regression coverage corrected. Local build, type checks, content checks and browser tests pass. Independent final review and CI on the fix commit gate merging. CompressionKIT follows this cleanup.
 
-Plan: keep authored MDX and navigation; retain API/notebook generation; remove unused MkDocs config/dependencies; verify content, links, downloads and browser behavior. Publish PR for review; do not merge without approval.
-
-Implementation: authored MDX and static public assets are tracked; navigation is direct Astro configuration; static redirects are checked in. API, notebook and example-download generation remain. Removed MkDocs config, converters, duplicate notebook sources and docs dependency group; lock regenerated. sleepKIT retains golden-contract evidence at docs/evidence and private notes in docs-maintainers.
-
-Verified: baseline and migrated route sets identical; all static assets, notebook downloads and example downloads byte-identical; authored MDX unchanged. Build/type/output checks passed, including generation-preserves-authored-source regression. Browser suites passed (heartKIT 8, sleepKIT 17). Clean install with generated outputs removed, build/type/output checks and visual review passed. No third-party lockfile package versions changed. heartKIT lock regeneration also aligns its project version with pyproject.toml. PR publication pending. EDGE cleanup is already landed. AOT MkDocs is an active exported-model offline-doc feature, intentionally retained.
+Ownership: edit astro-site/src/content/docs for authored pages, src/navigation.mjs for navigation and notebooks/ for notebook sources. See astro-site/README.md for generated paths and validation commands.
diff --git a/astro-site/README.md b/astro-site/README.md
index 88faa21..196e7a1 100644
--- a/astro-site/README.md
+++ b/astro-site/README.md
@@ -1,6 +1,6 @@
 # sleepKIT documentation site
 
-The Astro/Starlight site reads the authored Markdown in `../docs` and the saved notebook in `../notebooks` and generates Python reference pages with Griffe. The Python package is inspected statically; no training dependencies or notebook execution are required.
+The Astro/Starlight site reads the authored Markdown/MDX in `src/content/docs/` and the saved notebook in `../notebooks` and generates Python reference pages with Griffe. The Python package is inspected statically; no training dependencies or notebook execution are required.
 
 Use Node 24, Python 3.12 and uv:
 
diff --git a/astro-site/scripts/canonical-content.test.mjs b/astro-site/scripts/canonical-content.test.mjs
index 27f7834..9926aa5 100644
--- a/astro-site/scripts/canonical-content.test.mjs
+++ b/astro-site/scripts/canonical-content.test.mjs
@@ -10,7 +10,7 @@ const walk = (dir) => readdirSync(dir, { withFileTypes: true }).flatMap((entry)
 const snapshot = () => Object.fromEntries([
   ...walk("src/content/docs").filter((p) => p.endsWith(".mdx") && !p.startsWith("src/content/docs/reference/")),
   "src/navigation.mjs", "src/redirects.json",
-  ...walk("public").filter((p) => !/^public\/(reference|notebooks|examples)\//.test(p)),
+  ...walk("public").filter((p) => !/^public\/(reference|notebooks|examples|evidence)\//.test(p)),
 ].map((p) => [relative(".", p), readFileSync(p).toString("base64")]));
 
 test("generation preserves authored pages, navigation, redirects and static assets", () => {
diff --git a/astro-site/scripts/check-output.mjs b/astro-site/scripts/check-output.mjs
index fed7452..8f4ae60 100644
--- a/astro-site/scripts/check-output.mjs
+++ b/astro-site/scripts/check-output.mjs
@@ -204,3 +204,14 @@ assert(
   !existsSync("public/examples/models-index-1.json"),
   "Model fragment should not be offered as a runnable configuration",
 );
+
+for (const path of files(resolve("../notebooks")).filter((p) => p.endsWith(".ipynb"))) {
+  const notebook = JSON.parse(readFileSync(path, "utf8"));
+  for (const cell of notebook.cells) {
+    if (cell.cell_type !== "markdown") continue;
+    const source = Array.isArray(cell.source) ? cell.source.join("") : cell.source;
+    for (const match of source.matchAll(/(?:github\.com|github)\/AmbiqAI\/sleepkit\/blob\/main\/([^\s)"<>]+)/g)) {
+      assert(existsSync(resolve("..", match[1])), `Notebook source link is missing: ${path} -> ${match[1]}`);
+    }
+  }
+}
diff --git a/astro-site/src/content/docs/modes/download.mdx b/astro-site/src/content/docs/modes/download.mdx
index 3454870..264a7d0 100644
--- a/astro-site/src/content/docs/modes/download.mdx
+++ b/astro-site/src/content/docs/modes/download.mdx
@@ -12,7 +12,7 @@ The `download` command is used to download all datasets specified. Please refer
 Using the CLI, the `download` command can be used to download specified datasets in the configuration file or directly in the command line.
 
 ```bash title="Terminal"
-sleepkit -m download -c '{"datasets": [{"name": "cmidss", "parameters": {"path": ".datatasets/cmidss"}}]}'
+sleepkit -m download -c '{"datasets": [{"name": "cmidss", "params": {"path": "./datasets/cmidss"}}]}'
 ```
 
 ### Python
diff --git a/docs-maintainers/detection-preparation-performance.md b/docs-maintainers/detection-preparation-performance.md
index bbb487b..491ba7d 100644
--- a/docs-maintainers/detection-preparation-performance.md
+++ b/docs-maintainers/detection-preparation-performance.md
@@ -58,7 +58,7 @@ not a controlled cross-backend or hardware performance certification.
 
 ## Recorded equivalence and extraction timing
 
-The [full training comparison](../astro-site/public/evidence/membership-vectorized-equivalence-20260920/report.json)
+The [full training comparison](../docs/evidence/membership-vectorized-equivalence-20260920/report.json)
 passed for all **14,857,307 feature frames from 193 training series**. Feature
 values, validity masks and endpoints matched byte for byte. The fitted normalizer
 fingerprint also equals the original CPU profile's fingerprint. Measurements used
@@ -82,7 +82,7 @@ no recordings, features or subject identifiers are included.
 
 ## Full recipe stage profile
 
-A [fresh-cache profile of the revised recipe](../astro-site/public/evidence/membership-vectorized-profile-20260920/report.json)
+A [fresh-cache profile of the revised recipe](../docs/evidence/membership-vectorized-profile-20260920/report.json)
 used the same frozen inputs and thread settings as the first profile. Its counts,
 fitted-normalizer fingerprint and resident input/target fingerprints match the
 baseline exactly: 34,474 training contexts in 1,078 batches, with ten contexts in
diff --git a/notebooks/archive/previous-docs-train-detect-model.ipynb b/notebooks/archive/previous-docs-train-detect-model.ipynb
index d4e5d71..0994735 100644
--- a/notebooks/archive/previous-docs-train-detect-model.ipynb
+++ b/notebooks/archive/previous-docs-train-detect-model.ipynb
@@ -8,12 +8,12 @@
     "<div class=\"grid cards\" markdown>\n",
     "\n",
     "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 488 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z\"/></svg>\n",
-    "<a href=\"https://colab.research.google.com/github/AmbiqAI/sleepkit/blob/main/docs/guides/train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
+    "<a href=\"https://colab.research.google.com/github/AmbiqAI/sleepkit/blob/main/notebooks/archive/previous-docs-train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
     "    View in Colab\n",
     "</a>\n",
     "\n",
     "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 496 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3 .3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5 .3-6.2 2.3zm44.2-1.7c-2.9 .7-4.9 2.6-4.6 4.9 .3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3 .7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3 .3 2.9 2.3 3.9 1.6 1 3.6 .7 4.3-.7 .7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3 .7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3 .7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z\"/></svg>\n",
-    "<a href=\"https://github.com/AmbiqAI/sleepkit/blob/main/docs/guides/train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
+    "<a href=\"https://github.com/AmbiqAI/sleepkit/blob/main/notebooks/archive/previous-docs-train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
     "    GitHub source\n",
     "</a>\n",
     "\n",
diff --git a/notebooks/train-detect-model.ipynb b/notebooks/train-detect-model.ipynb
index 5c4b9b9..8a3b008 100644
--- a/notebooks/train-detect-model.ipynb
+++ b/notebooks/train-detect-model.ipynb
@@ -8,12 +8,12 @@
     "<div class=\"grid cards\" markdown>\n",
     "\n",
     "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 488 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z\"/></svg>\n",
-    "<a href=\"https://colab.research.google.com/github/AmbiqAI/sleepkit/blob/main/docs/guides/train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
+    "<a href=\"https://colab.research.google.com/github/AmbiqAI/sleepkit/blob/main/notebooks/train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
     "    View in Colab\n",
     "</a>\n",
     "\n",
     "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 496 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3 .3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5 .3-6.2 2.3zm44.2-1.7c-2.9 .7-4.9 2.6-4.6 4.9 .3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3 .7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3 .3 2.9 2.3 3.9 1.6 1 3.6 .7 4.3-.7 .7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3 .7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3 .7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z\"/></svg>\n",
-    "<a href=\"https://github.com/AmbiqAI/sleepkit/blob/main/docs/guides/train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
+    "<a href=\"https://github.com/AmbiqAI/sleepkit/blob/main/notebooks/train-detect-model.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
     "    GitHub source\n",
     "</a>\n",
     "\n",