Data validation
skill for AI agents.
Check lineage, methods, calculations, bias, visualizations, and whether the stated conclusion actually follows from the available evidence.
What you get,
and what it checks.
Validate the evidence before a decision depends on it.
This skill gives analytical workflows a repeatable quality gate. The agent records source lineage, tests the calculation and methodology, surfaces missing evidence, and clearly separates supported conclusions from unresolved assumptions before a report reaches an accountable reviewer.
You get
- A lineage and methodology check
- Recalculated figures and explicit assumptions
- A clear supported-versus-uncertain conclusion
It checks
- Source evidence is authorized and traceable
- Calculations can be reproduced
- A human reviews consequential conclusions
Where teams use it.
With the tools they connect.
Questions about
data validation.
What does the data validation skill do?
Check lineage, methods, calculations, bias, visualizations, and whether the stated conclusion actually follows from the available evidence.
What does it check?
Source evidence is authorized and traceable; calculations can be reproduced; a human reviews consequential conclusions.
Which tools does it work with?
Sentry, Cloudflare Developer Platform, Slack, Notion, Airtable, Google Drive, Outlook and GitHub.
How do I add it to an agent?
Turn it on in the agent's settings or for a project. Then reference it in chat with @.
