label-quality-audit
label-quality-audit is a Non-code artifact review skill for AI agents, published by mkurman in the zorai repository on GitHub. Skillspace rates label-quality-audit D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Audit label quality using confident learning (Northcutt et al.), cross-validation noise detection, and per-class error analysis. Identifies mislabeled examples for review.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 320
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 1 collections
- Scope
- Portable: works in any project. Generic confident-learning label noise audit for any dataset
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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