adversarial-review
adversarial-review is a Codebase audits & code health skill for AI agents, published by DanMcInerney in the architect-loop repository on GitHub. Skillspace rates adversarial-review D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Use when the architect factory orchestrator dispatches a fresh strategist subagent to harden a draft spec: falsify it with file:line evidence, fold the surviving findings into a revised spec, decompose it (issues plus checks), and stress-test its own decomposition before returning all drafts. Never commits, touches the tracker, or edits product code.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 615
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
- 84 / 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
- Category is a classifier prediction; no model has read this skill 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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