Motherduck enable self serve analytics
Skill motherduckdb/agent-skills/skills/motherduck-enable-self-serve-analytics
Roll out self-serve analytics on MotherDuck for internal teams. Use when deciding the first governed dataset, the first Dive or share, ownership boundaries, and the rollout path from one audience to broader adoption.From its SKILL.md
npx -y skills add motherduckdb/agent-skills --skill motherduck-enable-self-serve-analyticsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
4.5 KB, as published. Nobody here has run it
Enable Self-Serve Analytics
Use this skill when the user wants broad internal access to analytics with clear guardrails, trusted datasets, and a practical rollout path.
This is a use-case skill. It orchestrates motherduck-explore, motherduck-query, motherduck-model-data, motherduck-create-dive, and motherduck-share-data.
Start Here: Is a MotherDuck Server Active?
- If a remote MotherDuck MCP server or local MotherDuck server is active, use it.
- Discover the target database or workspace from the active context. Ask only when multiple plausible targets remain and the choice would materially change the rollout.
- Explore the live data model before defining the rollout:
- trusted source tables
- candidate curated views
- department-level dimensions
- core KPIs
- share boundaries
Use the actual data model to pick the first audience and first asset.
If no server is active, use any supplied schema and audience context. For planning work, proceed with explicit assumptions when safe; ask for missing details only when they block a reliable result.
Rollout Defaults
- first audience first, not company-wide exposure
- curated dataset before broad access
- Dive or share boundary over raw table dumping
- standard ownership for metric changes
- lightweight metric definitions and owners before inviting more users
Workflow
- Inspect the available MotherDuck server or supplied schema context.
- Inspect the data model that internal teams would use.
- Pick the first audience and first use case.
- Publish one trusted dataset.
- Document the metric owner, refresh expectation, and access boundary.
- Publish one Dive or one share.
- Expand only after the first workflow is stable.
Match execution to the request: answer, review, or planning work returns the requested rollout artifacts; build or change work creates the requested in-scope dataset, Dive, or share and validates it. Ask before broader access grants, destructive changes, or external writes not already authorized.
When this skill produces a native DuckDB (md:) connection, watermark it with custom_user_agent=agent-skills/2.5.0(harness-<harness>;llm-<llm>). If metadata is missing, fall back to harness-unknown and llm-unknown.
Output
The output of this skill should be:
- the first audience
- the first asset
- the governing dataset
- the ownership model
- the rollout guardrails
If the caller explicitly asks for structured JSON, return raw JSON only with no Markdown fences or prose before/after it. This is mainly for automated tests, regression checks, or downstream tooling that needs a stable machine-readable shape. Normal human-facing use of the skill can stay in prose unless JSON is explicitly requested.
Use this exact top-level shape when JSON is requested:
{
"summary": {},
"assumptions": [],
"implementation_plan": [],
"validation_plan": [],
"risks": []
}
References
Read this as reference, not as a script to execute:
references/SELF_SERVE_ROLLOUT_GUIDE.md-- curate-publish-expand sequence, Dive-versus-share choice, data freshness checks, scale guidance, and starter snippets
Runnable Artifact
artifacts/self_serve_rollout_example.py-- MotherDuck-backed Python example that publishes a curated view and produces team KPI output for a first rollout assetartifacts/self_serve_rollout_example.ts-- TypeScript companion artifact with the same rollout output contract
Run it with:
uv run --with duckdb python skills/motherduck-enable-self-serve-analytics/artifacts/self_serve_rollout_example.py
Run the same artifact against a temporary MotherDuck database:
MOTHERDUCK_ARTIFACT_USE_MOTHERDUCK=1 \
uv run --with duckdb python skills/motherduck-enable-self-serve-analytics/artifacts/self_serve_rollout_example.py
Validate the TypeScript companion artifact:
uv run scripts/test_typescript_artifacts.py
Related Skills
motherduck-explore-- inspect the real workspace before rolloutmotherduck-query-- validate KPI definitionsmotherduck-model-data-- publish curated analytical views or tablesmotherduck-create-dive-- build the first shareable answer surfacemotherduck-share-data-- publish governed data access when users need SQL, not just a Dive
What ships with it: 3 files
12.9 KB alongside SKILL.md, 2 of them executable
artifacts/
- self_serve_rollout_example.pyruns2.1 KB
- self_serve_rollout_example.tsruns1.7 KB
references/
Gives 0 of the 12 instructions most analytics metrics skills give
Counted across 333 of the 342 authors here whose files we hold, read 2026-09-06
- Read product marketing context before asking questionsin 37 of 333, across 16 files
- Test one variable at a timein 26 of 333, across 11 files
- Pre-determine sample size before launchin 24 of 333, across 16 files
- Verify tracking and QA variants before launchin 17 of 333, across 8 files
- Monitor for technical issues during the testin 14 of 333, across 6 files
- Match each save offer to the cancel reasonin 14 of 333, across 5 files
- Start every test with a specific hypothesisin 14 of 333, across 7 files
- Keep the continue-cancelling option visiblein 13 of 333, across 4 files
- Document every test with hypothesis, variants, results, and learningsin 13 of 333, across 6 files
- Gather churn, billing, product, usage, and constraint context firstin 12 of 333, across 3 files
- Build a health score from weighted signalsin 12 of 333, across 3 files
- Retry soft declines 3-5 times over 7-10 daysin 12 of 333, across 3 files
Said here and by no other author read
- Inspect the active server or supplied schema
- Inspect the data model internal teams would use
- Discover the target database from active context
- Pick the first audience and first use case
- Publish one trusted dataset
- Document metric owner, refresh expectation, and access boundary
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.