Sub-skill of data-validation: Data Quality Checks (+3).
Scanned 9/9/2026
Install to Claude Code
npx -y skills add vamseeachanta/workspace-hub --skill data-quality-checks --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Data Quality Checks?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/vamseeachanta-data-quality-checks)More formats (shields.io, HTML) on the badges page.
---
name: data-validation-data-quality-checks
description: 'Sub-skill of data-validation: Data Quality Checks (+3).'
version: 1.0.0
category: data-analytics
type: reference
scripts_exempt: true
---
# Data Quality Checks (+3)
## 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 the same time period length. Partial periods are excluded or noted.
- [ ] **Join correctness**: JOIN types are appropriate (INNER vs LEFT). Many-to-many joins haven't inflated counts.
- [ ] **Metric definitions**: Metrics match how stakeholders define them. Any deviations are noted.
- [ ] **Subtotals sum**: Parts add up to the whole where expected. If they don't, explain why (e.g., overlap).
## Reasonableness Checks
- [ ] **Magnitude**: Numbers are in a plausible range. Revenue isn't negative. Percentages are between 0-100%.
- [ ] **Trend continuity**: No unexplained jumps or drops in time series.
- [ ] **Cross-reference**: Key numbers match other known sources (dashboards, previous reports, finance data).
- [ ] **Order of magnitude**: Total revenue is in the right ballpark. User counts match known figures.
- [ ] **Edge cases**: What happens at the boundaries? Empty segments, zero-activity periods, new entities.
## Presentation Checks
- [ ] **Chart accuracy**: Bar charts start at zero. Axes are labeled. Scales are consistent across panels.
- [ ] **Number formatting**: Appropriate precision. Consistent currency/percentage formatting. Thousands separators where needed.
- [ ] **Title clarity**: Titles state the insight, not just the metric. Date ranges are specified.
- [ ] **Caveat transparency**: Known limitations and assumptions are stated explicitly.
- [ ] **Reproducibility**: Someone else could recreate this analysis from the documentation provided.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!