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name: analysis-qa-checklist description: Pre-delivery quality assurance for analysis work. Use when reviewing analysis before sharing with stakeholders, checking for completeness, validating assumptions, or ensuring clarity of recommendations.

When to use

Before sharing any analysis output with a stakeholder — dashboard, report, ad-hoc query result, model output, or written findings. Run this every time, not just for big projects. The cost of a post-delivery correction is always higher than the cost of a pre-delivery check.

Process

1. Run automated checks — use scripts/qa_runner.py against the output file to catch numeric, structural, and formatting issues programmatically.
2. Complete the logic checklist — work through references/qa_checklist_master.md section by section: question framing, data sourcing, transformations, statistical validity, findings, and presentation.
3. Review for common errors — cross-check against references/common_analysis_errors.md; pay special attention to the top-frequency mistakes for the analysis type.
4. Validate assumptions explicitly — for every assumption in the analysis, verify it has a source, is documented, and the output is sensitivity-tested where the assumption is uncertain.
5. Check the narrative — confirm the conclusion follows from the data, caveats are stated, and the recommendation is actionable.
6. Record sign-off — complete assets/qa_signoff_template.md with reviewer, issues found, resolution status, and delivery decision.

Inputs the skill needs

• Output file to review (CSV, notebook, SQL result, or written doc)
• Original analysis question / brief
• Name of reviewer and intended audience

Output

• QA runner report (automated flags)
• Completed checklist with pass/fail per section
• Signed-off qa_signoff_template.md confirming delivery readiness