SourceMedium Agent Skills

Installable skills for coding agents that work with SourceMedium BigQuery data.

sm is the SourceMedium shorthand. Skills use sm-<job> names so Claude Code, Codex, OpenClaw-style agents, and other skill clients can route to them predictably while display metadata spells out the SourceMedium brand.

Quick Start (Copy/Paste)

Copy this prompt and give it to your coding agent:

Install and use the SourceMedium BigQuery Analyst skill.

Context:
- SourceMedium hosts analytics-ready ecommerce data in BigQuery.
- My SourceMedium project ID is: [YOUR_PROJECT_ID]
- Default datasets are sm_metadata and sm_transformed_v2 unless I provide custom dataset names.
- I may also have my own BigQuery tables that I want to join to SourceMedium data.

Tasks:
1. Install: npx skills add source-medium/skills --skill sm-bigquery-analyst
2. Run the skill's setup verification.
3. Discover available SourceMedium tables, freshness, stores, and relevant metrics.
4. Answer my question with a SQL receipt, dry-run bytes, assumptions, and caveats.

My first question is: [ASK YOUR QUESTION]

Install With skills

npx skills add source-medium/skills --skill sm-bigquery-analyst
npx skills add source-medium/skills --skill sm-dashboard-builder
npx skills add source-medium/skills --skill sm-pipeline-builder

Repo-local commands below assume you are in this repository root.

Update Installed Skills

If you installed with the skills CLI, update to the latest published version with:

npx skills update sm-bigquery-analyst -y
npx skills update sm-dashboard-builder -y
npx skills update sm-pipeline-builder -y

To update project-scoped or global skills explicitly:

npx skills update sm-dashboard-builder --project -y
npx skills update sm-dashboard-builder --global -y

If your agent installed by copying folders instead of using the CLI, replace the copied skill directory with the latest skills/<skill-name>/ directory from this repository, then rerun the QA command for that skill.

See COMPATIBILITY.md for copy-based installs and agent-specific notes, and CHANGELOG.md for update contents.

Generic Install

For agents that support the Agent Skills open format, copy skills/<skill-name>/ into the agent's configured skills directory. Examples:

# Claude Code project skill
mkdir -p .claude/skills
cp -R skills/sm-bigquery-analyst .claude/skills/
cp -R skills/sm-dashboard-builder .claude/skills/
cp -R skills/sm-pipeline-builder .claude/skills/

# Personal skill
mkdir -p ~/.claude/skills
cp -R skills/sm-bigquery-analyst ~/.claude/skills/
cp -R skills/sm-dashboard-builder ~/.claude/skills/
cp -R skills/sm-pipeline-builder ~/.claude/skills/

Codex/OpenAI-compatible clients can also read the packaged agents/openai.yaml metadata when their skill registry supports it.

Available Skills

Skill	Description
sm-bigquery-analyst	Query SourceMedium BigQuery safely, discover warehouse metadata, and join operator-owned tables with SourceMedium metrics.
sm-dashboard-builder	Build accurate BI dashboards from SourceMedium BigQuery data, defaulting to portable HTML with SQL receipts and renderer-appropriate charts.
sm-pipeline-builder	Spec, build, validate, and operate bespoke data pipelines that land in customer-owned BigQuery datasets alongside SourceMedium data, or publish those tables back out to a system you own.

After Installing

Ask your coding agent questions like:

What was my revenue by channel last month?

Show me new customer acquisition by source over the past 30 days

What's my customer LTV by cohort?

Your agent will verify access, generate SQL, and return an auditable "SQL receipt".

For dashboard work, ask:

Build an HTML executive dashboard for net revenue, orders, AOV, channel mix,
ad spend, and MER for the last 30 days.

Create Metabase-ready cards for CAC, MER, spend, and revenue by channel.

The dashboard skill should discover metadata, define metric contracts, dry-run each BI query, execute bounded queries, and then build dashboard.html from a manifest with chart specs, embedded data, QA notes, freshness, and SQL receipts. The bundled HTML builder uses Vega-Lite for portability, but agents should use Metabase/native BI settings or an existing app chart library when that is the target.

For publishable dashboards, agents should validate the manifest in strict mode before rendering or handoff:

python skills/sm-dashboard-builder/scripts/validate_dashboard_manifest.py \
  skills/sm-dashboard-builder/assets/dashboard_manifest_template.json \
  --strict

Use dashboard_publish_checklist.md as the final share/handoff checklist.

For hybrid analysis, add a project-local sourcemedium_custom_data.md describing your own tables' grain, join keys, date coverage, owner/source, caveats, and PII columns before asking the agent to join them to SourceMedium data.

For bespoke pipeline work, ask:

Build a nightly pipeline that ingests our loyalty platform into our own
BigQuery datasets and joins point liability to SourceMedium revenue.

The pipeline skill writes a validated pipeline spec first (grain, keys, cursor, windows, checks), then builds, validates with a canary plus source-parity totals, and hands over a readiness checklist.

What a New Agent Needs to Know

SourceMedium data is already modeled for analysis. A cold coding agent should not guess table names, columns, categorical values, metric definitions, or tenant layout. It should:

1. Verify gcloud/bq auth and the active BigQuery project.
2. Read sm_metadata.dim_data_dictionary to discover available tables and freshness.
3. Read sm_metadata.dim_semantic_metric_catalog to resolve metric names and aliases.
4. Use sm_transformed_v2 for core analytics tables such as obt_orders, obt_order_lines, obt_customers, rpt_ad_performance_daily, and rpt_cohort_ltv_*.
5. Dry-run every new query and enforce a bytes-billed cap before execution.

Most tenants use datasets named sm_metadata and sm_transformed_v2. If your warehouse uses tenant-prefixed datasets in a shared project, pass those dataset names to the helper scripts:

python skills/sm-bigquery-analyst/scripts/sm_bq_doctor.py \
  --project <project> \
  --metadata-dataset <tenant>_sm_metadata \
  --transformed-dataset <tenant>_sm_transformed_v2

QA Harness

After editing any skill, run the package QA:

python scripts/qa_all_skills.py

With live demo access:

python scripts/qa_all_skills.py --project sm-democo

For targeted analyst QA:

python skills/sm-bigquery-analyst/scripts/qa_sm_bigquery_skill.py

With live demo access:

python skills/sm-bigquery-analyst/scripts/qa_sm_bigquery_skill.py --project sm-democo

The harness validates package shape, script syntax, help output, unsafe-SQL rejection, optional live metadata discovery, dry-runs, and cost-cap blocking. --project here is a connectivity and schema smoke test, so the demo warehouse is fine for it. Do not use the demo warehouse to check whether an answer is correct — its values are obfuscated. Validate numbers against a real tenant warehouse instead.

For dashboard package QA:

python skills/sm-dashboard-builder/scripts/qa_sm_dashboard_skill.py
python skills/sm-dashboard-builder/scripts/validate_dashboard_manifest.py \
  skills/sm-dashboard-builder/assets/dashboard_manifest_template.json \
  --strict
python skills/sm-dashboard-builder/scripts/build_dashboard_html.py \
  skills/sm-dashboard-builder/assets/dashboard_manifest_template.json \
  --out /tmp/sm-dashboard.html

For pipeline package QA:

python skills/sm-pipeline-builder/scripts/qa_sm_pipeline_skill.py
python skills/sm-pipeline-builder/scripts/validate_pipeline_spec.py \
  skills/sm-pipeline-builder/assets/pipeline_spec_template.yaml \
  --strict

The harness covers package shape, reference routing, a credential scan over the package, and the spec validator's full accept/reject matrix. It also fuzzes every path in the spec template with every wrong type, so a missing shape guard fails QA here instead of surfacing as a traceback in front of an operator. When writing a real pipeline, validate your per-pipeline spec copy, not the shipped template.

Documentation

• Agent Skills Overview
• SM BigQuery Analyst
• SM Dashboard Builder
• BigQuery Access Request Template