adversarial-audit
adversarial-audit is a Codebase audits & code health skill for AI agents, published by darkroomengineering in the cc-settings repository on GitHub. Skillspace rates adversarial-audit D (65/100) and it passed the safety scan; the rating is based on static analysis only.
Adversarial whole-repo audits in three goal-spec modes — hunt defects, drift, and dead ends rather than validate. Triggers "adversarial audit", "fable audit", "audit the codebase", "expectation gaps" (codebase mode - correctness, incoherences, affordance mismatches); "audit the docs", "docs audit", "documentation audit", "doc drift" (docs mode - accuracy vs code, inverted pyramid, doc sizing, diagram backlog); "process audit", "audit the workflows", "walk the journeys", "end-to-end audit" (process mode - empirical journey walking, state-machine map, dead ends, re-run semantics). Sibling to /nuclear-review (maintainability + code-judo deletion) - this skill audits whether the system does what it promises, not whether it should exist.
- Grade
- D
- Quality
- 65 / 100
- Safety
- Passed
- GitHub stars
- 39
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 94 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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