cross-modal-review

A cross-model review requires a second AI model to independently verify AI-generated outputs, analyses, and decisions before finalization. It works by loading the originating skill's contract, routing the output through a chain of models, and transparently reporting the final verdict.

When a model declines a review, the system employs silent refusal routing to automatically switch models within the configured chain. If all models fail, it transparently escalates the issue to the user, ensuring the quality gate is never silently bypassed.

Yes, cross-model checks load and compare the originating skill's Contract to verify promised guarantees. The reviewer evaluates the output against this contract and delivers a structured report detailing the reviewer, contract status, final verdict, and supporting evidence.

The best way to enforce a quality gate is requiring a second-model review before finalizing AI-generated decisions and texts. This approach applies independent verification and cross-model checks, utilizing model routing, contract loading, and grading to transparently resolve output quality.

A key limitation is complete dependency on the review chain's availability; if all models in the routing chain decline the task, the system cannot provide a verdict. This constraint requires escalating the unfinished review process directly to the user.