adversarial-review

Adversarial code review uses AI models to critique code from opposing perspectives, identifying flaws missed by a single model. You initiate a review for recent code changes, spawning reviewers on distinct models via specific CLIs to challenge code quality.

Cross-model AI reviews are ideal after significant code refactoring, large diff analysis, or plan implementation phases. They provide unbiased critical feedback from an AI operating on different training data, ensuring robustness and adherence to best practices.

Yes, cross-model code reviews require execution via specific model command line interfaces like `codex exec` or `claude -p`. This ensures a true adversarial perspective is generated by the opposing model rather than relying on subagent delegation.

Adversarial code review spawns AI reviewers on opposing models, such as Claude reviewing Codex code and vice versa, to provide diverse critical lenses. Standard AI reviews typically use a single model, potentially missing flaws due to shared assumptions and training data.

Adversarial testing delivers a synthesized verdict with findings categorized by severity and mapped to specific principles. This structured feedback helps you systematically address potential issues uncovered during the cross-model review process.