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