data-validation
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
npx skills add majiayu000/claude-skill-registry --skill data-validation-yongjianwan-agentskill --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Validation Skill Pre-delivery QA checklist, common data analysis pitfalls, result sanity checking, and documentation standards for reproducibility. ## Pre-Delivery QA Checklist Run through this checklist before sharing any analysis with stakeholders. ### Data Quality Checks - [ ] **Source verification**: Confirmed which tables/data sources were used. Are they the right ones for this question? - [ ] **Freshness**: Data is current enough for the analysis. Noted the "as of" date. - [ ] **Completeness**: No unexpected gaps in time series or missing segments. - [ ] **Null handling**: Checked null rates in key columns. Nulls are handled appropriately (excluded, imputed, or flagged). - [ ] **Deduplication**: Confirmed no double-counting from bad joins or duplicate source records. - [ ] **Filter verification**: All WHERE clauses and filters are correct. No unintended exclusions. ### Calculation Checks - [ ] **Aggregation logic**: GROUP BY includes all non-aggregated columns. Aggregation level matches the analysis grain. - [ ] **Denominator correctness**: Rate and percentage calculations use the right denominator. Denominators are non-zero. - [ ] **Date alignment**: Comparisons use t
- Pre-Delivery QA Checklist
- Data Quality Checks
- Calculation Checks
- Reasonableness Checks
- Presentation Checks
- Common Data Analysis Pitfalls
- Join Explosion
- Survivorship Bias
- Incomplete Period Comparison
- Denominator Shifting
- Average of Averages
- Timezone Mismatches
- Selection Bias in Segmentation
- Result Sanity Checking
What does the data-validation skill do?
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-validation-yongjianwan-agentskill --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.