cross-modal-review

To get a second AI model review, you route your code diffs to a different model for independent evaluation against the originating contract. This cross-model evaluation provides transparent disagreement or approval signals before finalization.

Cross-model evaluation is a quality gate process that routes work output to a different AI model for validation against a contract. It ensures independent verification by providing transparent agreement or disagreement signals before finalizing code or content.

When an AI model refuses to evaluate your code during an automated review, the cross-modal review process implements silent routing. This automatically moves the evaluation task to an alternative reviewer model without interrupting your workflow.

Yes, you can use automated contract-based evaluation for content other than code. The cross-model review process applies to brain-page content, code diffs, and any other output that requires validation before finalization.

Yes, cross-model review works without setting up external dependencies. The skill operates independently to route work output to a second model and enforce contract-based evaluation by providing transparent findings for your decision.

A key limitation of using a different model for code review validation is the potential for model refusal. However, the process mitigates this by using silent routing to automatically move to alternative reviewers when a model refuses the evaluation task.