data-audit
Skill4 starsChanged 4 months agoLicence not identifiedView on GitHub
Use this skill to audit and cross-validate a data-cleaning result before trusting it, especially work produced by an AI/agent. Given the cleaning code plus before/after data, it checks the empirical red lines: is every change in sample size accounted for (a sample-selection log); is the merge match rate normal (no silent losses); were outliers blanket-winsorized instead of investigated; are variable definitions/units consistent and summary statistics in line with published papers on similar samples; and above all, were any samples dropped or thresholds picked to favor a desired result. It never modifies data; it only flags suspicious points for the researcher to review.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- 26zl/universal-agent-skillstheir own version, first seen Oct 13, 2026
- TheWatcher01/skillstheir own version, first seen Apr 1, 2026
Other versions2 projects use a version of this.
Word for word · 0
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Wrote their own version · 2
- 26zl/universal-agent-skills★ 2same name
- TheWatcher01/skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository7 other files it gives its agents.
- paper-contextSkill
- paper-strategySkill
- replication-navigatorSkill
- data-cleaning-plannerSkill
- regression-interpreterSkill
- repro-loggerSkill
- stata-runnerSkill
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