# Adversarial Review

> Fresh adversarial code review with binary PASS/FAIL verdicts, evidence citations, and anchoring bias prevention via fresh reviewer spawning.

- Skill: `a5c-ai/adversarial-review` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add a5c-ai/adversarial-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/adversarial-review/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/adversarial-review

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- For final comprehensive cross-unit review
- When verifying spec compliance of any implementation

## Key Differences from Collaborative Review

| Aspect | Collaborative | Adversarial |
|--------|--------------|-------------|
| Goal | Help improve code | Verify spec compliance |
| Verdict | Suggestions | Binary PASS/FAIL |
| Evidence | Optional | Required (file:line) |
| Reviewer | Can be reused | Must be fresh |
| Context | Shared | Independent |

## Fresh Reviewer Rule

On re-review after FAIL, a NEW reviewer instance spawns with no memory of the previous review. This prevents anchoring bias where a reviewer fixates on previously identified issues.

## Anti-Patterns

- Reusing reviewers after FAIL
- Passing previous findings to new reviewers
- Providing subjective or advisory feedback
- Accepting partial compliance as PASS

## Tool Use

Invoke as part of: `methodologies/metaswarm/metaswarm-execution-loop` (Phase 3)