experiment-audit
experiment-audit is a Multi-agent review & consensus skill for AI agents, published by wanshuiyin in the Auto-claude-code-research-in-sleep repository on GitHub. Skillspace rates experiment-audit D (65/100) and it passed the safety scan; the rating is based on static analysis only.
Audit experiment integrity before claiming results. Uses cross-model review (external reviewer backend) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says "审计实验", "check experiment integrity", "audit results", "实验诚实度", or after experiments complete before writing claims.
- Grade
- D
- Quality
- 65 / 100
- Safety
- Passed
- GitHub stars
- 13,451
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
- 91 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 1 collections
- Scope
- Portable: works in any project. General cross-model experiment integrity audit, no repo-specific code
What the scanner flagged
- over-broad allowed-tools ('*')
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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