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analysis-qa-checklist

analysis-qa-checklist

github.com/nimrodfisherData & databases504229 installsUpdated 5w ago
Repository

About analysis-qa-checklist

A quality-assurance skill for reviewing analysis before delivery, covering reports, dashboards, query results, model outputs, and written findings. It combines automated checks, a logic checklist, common-error references, and a sign-off template.

Key features

  1. 01Run automated file checks
  2. 02Complete a logic checklist
  3. 03Check for common analysis errors
  4. 04Validate and document assumptions
  5. 05Review conclusions and recommendations
  6. 06Record reviewer sign-off

Use cases

  1. 01Check a dashboard or report before delivery
  2. 02Review ad hoc query and model outputs
  3. 03Confirm findings are supported and caveats are stated

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill analysis-qa-checklist

After installing, type/analysis-qa-checklistin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
qa_signoff_template.md
references/
common_analysis_errors.md
qa_checklist_master.md
scripts/
qa_runner.py

Details

From the project README

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

Comments

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