data-cleanup
Clean and standardize messy tabular data (CSV, spreadsheet paste, system exports) into an analysis-ready dataset — consistent dates and names, typed columns,…
Turn a process description into an operational quality-control checklist — binary pass/fail checks, acceptance criteria, critical-vs-routine tiers, and failure modes with catch-points. Use when the user says "build a QC checklist", "standardize how we review X", "acceptance
$ npx -y skills add sgharlow/claude-code-recipes --skill qc-checklist --agent claude-codeHow it fires
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Turn a process description into an operational quality-control checklist — binary pass/fail checks, acceptance criteria, critical-vs-routine tiers, and failure modes with catch-points. Use when the user says "build a QC checklist", "standardize how we review X", "acceptance
name: qc-checklist description: Turn a process description into an operational quality-control checklist — binary pass/fail checks, acceptance criteria, critical-vs-routine tiers, and failure modes with catch-points. Use when the user says "build a QC checklist", "standardize how we review X", "acceptance criteria for this process", or describes quality varying by who does the work.
Convert "experienced people just know" into checks anyone can run. A good check is binary (pass/fail), observable (no judgment calls hidden inside), and placed at the point where the failure it catches actually happens.
1. Get the process: what's produced, the steps, who performs it, and — most important — what has actually gone wrong before. Real past failures anchor the checklist; ask for 2–3 if none are offered. 2. Draft the failure-mode inventory first: for each step, what can go wrong, how it would be detected, and the cost of missing it. Every checklist item must trace to a failure mode — a check that catches nothing is process theater. 3. Build the checklist in two tiers:
are never skippable and each states its acceptance criterion measurably.
Phrase every item as a verifiable pass/fail question with the evidence to look at — "Invoice total matches PO total (compare field X to field Y)", not "check invoice". 4. Add the operating shell: when the checklist runs (per unit / per batch / per release), who signs off, where results are recorded, and the escalation when a critical check fails. A checklist without a recorded result is a suggestion. 5. Verify by dry-run: walk one recent real work product through the draft. Every past failure the user named must be caught by some item; items that catch nothing on the dry-run get justified or cut. Then hand over with a 90-day review note — checklists drift as processes change.
"appropriate", "sufficient", or "high quality".
rather than shipping a wall nobody completes under time pressure.
Full walkthrough, examples, and variations: `recipes/Recipe-048-Quality-Control-Checklists-Standards.md`. For enforcing checks on AI-assisted code specifically, the same philosophy is implemented in [ai-control-framework](https://github.com/sgharlow/ai-control-framework).
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Repo: sgharlow/claude-code-recipes
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