adversarial-review

Adversarial code review orchestrates cross-model critique by spawning reviewers on the opposite model to attack work from distinct lenses. Trigger it after large diffs or planning sessions to obtain a synthesized verdict and per-lens findings without altering the source.

Cross-model AI code critique spawns reviewers on the opposite model, such as Claude versus Codex, to challenge work. It surfaces diverse failure modes by grounding each critique in a specific brain principle and providing concrete remediation.

Use an opposite-model review when standard reviews miss edge cases after large diffs, planning phases, or cook sessions. It enforces cross-model CLI execution with standardized prompts to reduce blind spots and improve review coverage.

Yes, the adversarial review workflow explicitly supports spawning reviewers on the opposite model, pitting Claude versus Codex. It requires opposite-model CLI execution to generate structured per-lens findings and a synthesized verdict.

Automated adversarial code review enforces outputs including a synthesized verdict and per-lens findings. Each critique maps to a specific principle and provides concrete remediation steps for actionable review coverage.

Adversarial code review does not alter the source. It orchestrates cross-model critique strictly to surface critical issues and generate a structured verdict, leaving your original codebase untouched.