$ ~/registry/skill/anthropics-data-skills-validate-data

SKILL

validate-data

Name: validate-data
Author: anthropics

Review an analysis for methodology, accuracy, bias, and evidence support.

Stars

★ 22,528

Source

GitHub

Updated

2026-07-11

// securityLow risk

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// install

Copy the install command and let the AI configure it · recommended for beginners

Please install the "validate-data" skill from askskill:
1. Download https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/data/skills/validate-data/SKILL.md
2. Save it as ~/.claude/skills/validate-data/SKILL.md
3. Reload skills and tell me it's ready

// download

Download SKILL.mdInstall manifest ↗

// examples

Pre-presentation analysis review

Prompt

Please review this analysis summary for leadership. Check whether the methodology is sound, metric definitions are consistent, conclusions are fully supported by the data, and identify potential biases or caveats to add.

Expected

A structured review highlighting methodology issues, evidence gaps, bias risks, and recommended revisions.

Spot-check calculations and aggregation logic

Prompt

Here are the key calculations and aggregation rules used in the analysis. Check for formula errors, double counting, inconsistent grouping logic, or unusual sample sizes, and explain which results should be recalculated.

Expected

An itemized issue list showing suspicious calculations, likely causes, and priority fixes.

Validate whether SQL results look reasonable

Prompt

I will provide a SQL query, field definitions, and sample results. Assess whether the output is trustworthy by reviewing filters, joins, deduplication logic, time windows, and result distributions, then list follow-up validation checks.

Expected

An assessment of SQL result reliability plus a checklist of recommended validation steps.

// docs

/validate-data - Validate Analysis Before Sharing

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Review an analysis for accuracy, methodology, and potential biases before sharing with stakeholders. Generates a confidence assessment and improvement suggestions.

Usage

/validate-data <analysis to review>

The analysis can be:

A document or report in the conversation
A file (markdown, notebook, spreadsheet)
SQL queries and their results
Charts and their underlying data
A description of methodology and findings

Workflow

1. Review Methodology and Assumptions

Examine:

Question framing: Is the analysis answering the right question? Could the question be interpreted differently?
Data selection: Are the right tables/datasets being used? Is the time range appropriate?
Population definition: Is the analysis population correctly defined? Are there unintended exclusions?
Metric definitions: Are metrics defined clearly and consistently? Do they match how stakeholders understand them?
Baseline and comparison: Is the comparison fair? Are time periods, cohort sizes, and contexts comparable?

2. Run the Pre-Delivery QA Checklist

Work through the checklist below — data quality, calculation, reasonableness, and presentation checks.

3. Check for Common Analytical Pitfalls

Systematically review against the detailed pitfall catalog below (join explosion, survivorship bias, incomplete period comparison, denominator shifting, average of averages, timezone mismatches, selection bias).

4. Verify Calculations and Aggregations

Where possible, spot-check:

Recalculate a few key numbers independently
Verify that subtotals sum to totals
Check that percentages sum to 100% (or close to it) where expected
Confirm that YoY/MoM comparisons use the correct base periods
Validate that filters are applied consistently across all metrics

Apply the result sanity-checking techniques below (magnitude checks, cross-validation, red-flag detection).

5. Assess Visualizations

If the analysis includes charts:

Do axes start at appropriate values (zero for bar charts)?
Are scales consistent across comparison charts?
Do chart titles accurately describe what's shown?
Could the visualization mislead a quick reader?
Are there truncated axes, inconsistent intervals, or 3D effects that distort perception?

6. Evaluate Narrative and Conclusions

Review whether:

Conclusions are supported by the data shown
Alternative explanations are acknowledged
Uncertainty is communicated appropriately
Recommendations follow logically from findings
The level of confidence matches the strength of evidence

7. Suggest Improvements

Provide specific, actionable suggestions:

Additional analyses that would strengthen the conclusions
Caveats or limitations that should be noted
Better visualizations or framings for key points
Missing context that stakeholders would want

8. Generate Confidence Assessment

Rate the analysis on a 3-level scale:

Ready to share -- Analysis is methodologically sound, calculations verified, caveats noted. Minor suggestions for improvement but nothing blocking.

Share with noted caveats -- Analysis is largely correct but has specific limitations or assumptions that must be communicated to stakeholders. List the required caveats.

Needs revision -- Found specific errors, methodological issues, or missing analyses that should be addressed before sharing. List the required changes with priority order.

Output Format

## Validation Report

### Overall Assessment: [Ready to share | Share with caveats | Needs revision]

### Methodology Review
[Findings about approach, data selection, definitions]

### Issues Found
1. [Severity: High/Medium/Low] [Issue description and impact]
2. ...

### Calculation Spot-Checks

…

View full docs ↗