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.
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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.
scripts/qa_runner.py against the output file to catch numeric, structural, and formatting issues programmatically.references/qa_checklist_master.md section by section: question framing, data sourcing, transformations, statistical validity, findings, and presentation.references/common_analysis_errors.md; pay special attention to the top-frequency mistakes for the analysis type.assets/qa_signoff_template.md with reviewer, issues found, resolution status, and delivery decision.qa_signoff_template.md confirming delivery readinessNo reviews yet. Tell others what you thought.
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Rigorous A/B test statistical analysis. Use when analyzing experiment results, calculating statistical significance, checking for sample ratio mismatch, or validating test design before launch.
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Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
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Structured, reproducible analysis documentation. Use when documenting analysis findings, creating analysis notebooks, ensuring reproducibility, or building analysis archives for future reference.
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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
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Post-analysis learning and process improvement. Use when completing major analysis projects, documenting lessons learned, or improving team analytical practices.
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Standard business metric calculation with industry benchmarks. Use when calculating SaaS metrics (MRR, churn, LTV, CAC), e-commerce KPIs, or product analytics metrics with proper definitions.
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