Validate Data
AnthropicOfficialPublished by Anthropic
- Skill
- Review and audit
- Data and AI
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The validate-data skill provides a structured, systematic approach to auditing analytical work. It helps users identify methodological flaws, calculation errors, and cognitive biases that could undermine the credibility of their findings. By walking through a rigorous checklist covering data quality, logic, visualization, and narrative, the skill ensures that conclusions are robust, reproducible, and clearly communicated.
What it does
- Performs a multi-step QA review of methodology, calculations, and data selection.
- Checks for common analytical pitfalls like join explosions, survivorship bias, and denominator shifting.
- Evaluates the clarity and accuracy of visualizations and charts.
- Assesses whether conclusions are logically supported by the provided data.
- Generates a confidence assessment and actionable improvement suggestions.
- Provides templates for documenting analysis to ensure long-term reproducibility.
When to use it
- Before presenting any data-driven analysis or report to stakeholders.
- When spot-checking complex SQL queries or aggregation logic.
- When you suspect potential bias or methodological errors in your findings.
- Before finalizing high-stakes business decisions based on analytical output.
Advantages
- Reduces the risk of sharing inaccurate or misleading data.
- Increases stakeholder trust through transparent communication of caveats.
- Standardizes the quality of analytical output across a team.
- Helps identify subtle errors like timezone mismatches or selection bias.
Usage examples
- “/validate-data Review this quarterly revenue analysis before I send it to the exec team: [analysis]”
- The agent performs a full audit of the revenue report, checking for calculation errors, methodology flaws, and clarity, then provides a confidence score and improvement suggestions.
- “/validate-data Check my churn analysis -- I'm comparing Q4 churn rates to Q3 but Q4 has a shorter measurement window”
- The agent identifies the 'Incomplete Period Comparison' pitfall and suggests how to normalize the data for a fair comparison.
- “/validate-data Here's a SQL query and its results for our conversion funnel. Does the logic look right? [query + results]”
- The agent reviews the SQL logic for potential join explosions or filter errors and verifies if the aggregation matches the intended funnel definition.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/validate-data/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip validate-data.zip -d .agents/skills
Documentation by Anthropic
/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
- [Metric]: [Verified / Discrepancy found]
- ...
### Visualization Review
[Any issues with charts or visual presentation]
### Suggested Improvements
1. [Improvement and why it matters]
2. ...
### Required Caveats for Stakeholders
- [Caveat that must be communicated]
- ...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 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.
Common Data Analysis Pitfalls
Join Explosion
The problem: A many-to-many join silently multiplies rows, inflating counts and sums.
How to detect:
-- Check row count before and after join
SELECT COUNT(*) FROM table_a; -- 1,000
SELECT COUNT(*) FROM table_a a JOIN table_b b ON a.id = b.a_id; -- 3,500 (uh oh)How to prevent:
Always check row counts after joins
If counts increase, investigate the join relationship (is it really 1:1 or 1:many?)
Use
COUNT(DISTINCT a.id)instead ofCOUNT(*)when counting entities through joins
Survivorship Bias
The problem: Analyzing only entities that exist today, ignoring those that were deleted, churned, or failed.
Examples:
Analyzing user behavior of "current users" misses churned users
Looking at "companies using our product" ignores those who evaluated and left
Studying properties of "successful" outcomes without "unsuccessful" ones
How to prevent: Ask "who is NOT in this dataset?" before drawing conclusions.
Incomplete Period Comparison
The problem: Comparing a partial period to a full period.
Examples:
"January revenue is $500K vs. December's $800K" -- but January isn't over yet
"This week's signups are down" -- checked on Wednesday, comparing to a full prior week
How to prevent: Always filter to complete periods, or compare same-day-of-month / same-number-of-days.
Denominator Shifting
The problem: The denominator changes between periods, making rates incomparable.
Examples:
Conversion rate improves because you changed how you count "eligible" users
Churn rate changes because the definition of "active" was updated
How to prevent: Use consistent definitions across all compared periods. Note any definition changes.
Average of Averages
The problem: Averaging pre-computed averages gives wrong results when group sizes differ.
Example:
Group A: 100 users, average revenue $50
Group B: 10 users, average revenue $200
Wrong: Average of averages = ($50 + $200) / 2 = $125
Right: Weighted average = (100*$50 + 10*$200) / 110 = $63.64
How to prevent: Always aggregate from raw data. Never average pre-aggregated averages.
Timezone Mismatches
The problem: Different data sources use different timezones, causing misalignment.
Examples:
Event timestamps in UTC vs. user-facing dates in local time
Daily rollups that use different cutoff times
How to prevent: Standardize all timestamps to a single timezone (UTC recommended) before analysis. Document the timezone used.
Selection Bias in Segmentation
The problem: Segments are defined by the outcome you're measuring, creating circular logic.
Examples:
"Users who completed onboarding have higher retention" -- obviously, they self-selected
"Power users generate more revenue" -- they became power users BY generating revenue
How to prevent: Define segments based on pre-treatment characteristics, not outcomes.
Other Statistical Traps
Simpson's paradox: Trend reverses when data is aggregated vs. segmented
Correlation presented as causation without supporting evidence
Small sample sizes leading to unreliable conclusions
Outliers disproportionately affecting averages (should medians be used instead?)
Multiple testing / cherry-picking significant results
Look-ahead bias: Using future information to explain past events
Cherry-picked time ranges that favor a particular narrative
Result Sanity Checking
Magnitude Checks
For any key number in your analysis, verify it passes the "smell test":
| Metric Type | Sanity Check |
|---|---|
| User counts | Does this match known MAU/DAU figures? |
| Revenue | Is this in the right order of magnitude vs. known ARR? |
| Conversion rates | Is this between 0% and 100%? Does it match dashboard figures? |
| Growth rates | Is 50%+ MoM growth realistic, or is there a data issue? |
| Averages | Is the average reasonable given what you know about the distribution? |
| Percentages | Do segment percentages sum to ~100%? |
Cross-Validation Techniques
Calculate the same metric two different ways and verify they match
Spot-check individual records -- pick a few specific entities and trace their data manually
Compare to known benchmarks -- match against published dashboards, finance reports, or prior analyses
Reverse engineer -- if total revenue is X, does per-user revenue times user count approximately equal X?
Boundary checks -- what happens when you filter to a single day, a single user, or a single category? Are those micro-results sensible?
Red Flags That Warrant Investigation
Any metric that changed by more than 50% period-over-period without an obvious cause
Counts or sums that are exact round numbers (suggests a filter or default value issue)
Rates exactly at 0% or 100% (may indicate incomplete data)
Results that perfectly confirm the hypothesis (reality is usually messier)
Identical values across time periods or segments (suggests the query is ignoring a dimension)
Documentation Standards for Reproducibility
Analysis Documentation Template
Every non-trivial analysis should include:
## Analysis: [Title]
### Question
[The specific question being answered]
### Data Sources
- Table: [schema.table_name] (as of [date])
- Table: [schema.other_table] (as of [date])
- File: [filename] (source: [where it came from])
### Definitions
- [Metric A]: [Exactly how it's calculated]
- [Segment X]: [Exactly how membership is determined]
- [Time period]: [Start date] to [end date], [timezone]
### Methodology
1. [Step 1 of the analysis approach]
2. [Step 2]
3. [Step 3]
### Assumptions and Limitations
- [Assumption 1 and why it's reasonable]
- [Limitation 1 and its potential impact on conclusions]
### Key Findings
1. [Finding 1 with supporting evidence]
2. [Finding 2 with supporting evidence]
### SQL Queries
[All queries used, with comments]
### Caveats
- [Things the reader should know before acting on this]Code Documentation
For any code (SQL, Python) that may be reused:
"""
Analysis: Monthly Cohort Retention
Author: [Name]
Date: [Date]
Data Source: events table, users table
Last Validated: [Date] -- results matched dashboard within 2%
Purpose:
Calculate monthly user retention cohorts based on first activity date.
Assumptions:
- "Active" means at least one event in the month
- Excludes test/internal accounts (user_type != 'internal')
- Uses UTC dates throughout
Output:
Cohort retention matrix with cohort_month rows and months_since_signup columns.
Values are retention rates (0-100%).
"""Version Control for Analyses
Save queries and code in version control (git) or a shared docs system
Note the date of the data snapshot used
If an analysis is re-run with updated data, document what changed and why
Link to prior versions of recurring analyses for trend comparison
Examples
/validate-data Review this quarterly revenue analysis before I send it to the exec team: [analysis]/validate-data Check my churn analysis -- I'm comparing Q4 churn rates to Q3 but Q4 has a shorter measurement window/validate-data Here's a SQL query and its results for our conversion funnel. Does the logic look right? [query + results]Tips
Run /validate-data before any high-stakes presentation or decision
Even quick analyses benefit from a sanity check -- it takes a minute and can save your credibility
If the validation finds issues, fix them and re-validate
Share the validation output alongside your analysis to build stakeholder confidence
Files
- ATTRIBUTION.mdAdded by AutoGineer906 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md14.6 KiB
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter, Needs Python
Security
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Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/knowledge-work-plugins · data/skills/validate-data · commit e050c2e
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from data/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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