adversarial-review

Adversarial code review improves validation by using two different AI models, Claude and Codex, to review code changes from contrasting perspectives. This cross-model collaboration ensures diverse viewpoints, surfacing potential issues a single reviewer might miss.

Yes, you need both Claude and Codex to perform an adversarial review. The process requires both models to run reviews on the opposing model, spawning reviewers based on change complexity to provide contrasting perspectives.

To trigger an AI-driven code review, use the "adversarial review" command after completing a code change or planning session. The system then spawns reviewers on the opposing model and provides a synthesized verdict based on their outputs.

A synthesized verdict in cross-model collaboration is the final output generated after both Claude and Codex review code from opposing viewpoints. It combines the adversarial perspectives to deliver a comprehensive validation of the code changes.

Yes, automated reviewers can handle varying size and complexity. The adversarial review process automatically spawns reviewers on the opposing model based on the specific change size and complexity, ensuring appropriate validation depth.

You should avoid using an adversarial AI approach when a single model perspective is sufficient, such as for trivial code modifications. The cross-model collaboration process is designed for comprehensive review where diverse validation perspectives are crucial.