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Requirements and ownership

Requirement Owner / actual path Boundary
Selective trigger; small fixes stay lightweight Five Skill descriptions, SessionStart route, global template Model routing is guidance; host-trigger evidence is recorded separately
Current local/online evidence and code reuse rigor-evidence, research-routing, papers; evidence/fetch CLI Receipt proves retrieval; Lead verifies primary claim, version and license
Complete causal implementation and architecture rigor-task, implementation-debugging/design No canned architecture or mandatory paper count
ML train/infer/data/mask/NaN/checkpoint/metric correctness ml-experiments, project template, declared run metrics/artifacts Project declares real split/runtime/DDP/thresholds; generic tests do not prove model learning
Resource preflight, naming, quiet long jobs compute-resources/naming, inspect_resources/check_run_id helpers External job ownership and GPU availability still need observation
Real acceptance and stale-result rejection tasks.py run/finish; frozen source/argv/artifact/metric contract Source/data/environment omitted from declared scope cannot be inferred
Zero-agent default; substantial audit/review only delegation, policy.delegate, agent plan Written gain is not a quantitative token-savings measurement
Models, effort, no-parent-history role templates, native_request, agent run Native receipt may not expose actual model; no claim of zero system/project context
Cap, overlapping writers, reuse/fresh boundary SQLite assignments/task scope, bind/close, fresh exec Arbitrary native calls outside CLI are not centrally enforced
Exact cleanup, obsolete weights, preserve unknowns artifacts reserve/register/retention/plan/apply Existing files require separate exact user-authorized review; external writers are not sandboxed
Resumption and interruption external SQLite, task resume, recover-run Crash before child identity needs manual inspection; no fake success recovery
Selective project memory, one canonical home rigor-continuity + memory/retrieval/state/consolidation/policy No automatic global memory edits or per-turn document touch
Obsidian/Serena navigation memory helpers and references Derived pointers, structural audit not semantic truth
Library retrieval/bootstrap/ingest/link-code/verify papers/catalog/converter references; installed PaperMeld CLI No copied parser; unavailable CLI blocks mutations, read-only catalog remains possible
Git checkpoint and preservation Git recovery + next_checkpoint helper Task-owned local commit only, no automatic push
Short answers without hiding failures global template, skill completion guidance Caveman-style brevity cannot erase material evidence or limitations
One plugin, no duplicate engines shared rigor package/scripts/references; five entry Skills External connectors reused only when relevant; no new MCP daemon
Global vs deep-learning instructions templates/global.md, templates/deep-learning.md, setup responsibility table Templates supplied, never silently installed

Original inputs: global and DualCMP AGENTS, code-implementation (9 reference topics/4 scripts), memory-maintenance (4 reference topics/3 scripts), papermeld (catalog/converter and CLI workflows), user transcript and current corrections. Engineering and memory references were reused with Rigor-relative paths; delegation was rewritten around the user's cost boundary. Shared helper implementations remain single copies. No private library configuration is distributed.

Integration references: official Codex plugin/skill/hook/subagent mechanisms; Ponytail's event-driven entry inspired short lifecycle routing rather than copying its orchestration. GitHub and Exa support evidence discovery when needed; no meaningless tool calls on ordinary local fixes.