analysis-qa-checklist
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
- Run automated checks — use
scripts/qa_runner.pyagainst the output file to catch numeric, structural, and formatting issues programmatically. - Complete the logic checklist — work through
references/qa_checklist_master.mdsection by section: question framing, data sourcing, transformations, statistical validity, findings, and presentation. - Review for common errors — cross-check against
references/common_analysis_errors.md; pay special attention to the top-frequency mistakes for the analysis type. - 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.
- Check the narrative — confirm the conclusion follows from the data, caveats are stated, and the recommendation is actionable.
- Record sign-off — complete
assets/qa_signoff_template.mdwith 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
More from nimrodfisher/data-analytics-skills
funnel-analysis
Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, comparing segments through a funnel, or optimizing user journeys.
45executive-summary-generator
Create concise executive summaries from detailed analysis. Use when preparing board decks, executive briefings, or condensing complex analysis into decision-ready formats for senior audiences.
41insight-synthesis
Transform data findings into compelling insights. Use when converting analysis results into actionable insights, connecting findings to business impact, or preparing insights for stakeholder communication.
41data-narrative-builder
Build compelling data-driven narratives. Use when presenting analysis results, creating stakeholder reports, or transforming a set of findings into a story that drives a specific decision or action.
40data-quality-audit
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.
39time-series-analysis
Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.
39