Agent skill · data analytics · anthropics
validate-data
QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.
Why this skill is useful
Provides a structured methodology for validating analyses, ensuring accuracy and bias checks that the AI wouldn't reliably generate on its own.
What it needs
About 9k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
/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 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/validate-data