multi-model-review

To use multiple LLMs for code review, you can run a parallel review skill that executes four distinct models simultaneously on your git-tracked changes. It then aggregates findings into a peer-reviewed summary to filter false positives and generate a consensus report.

Yes, you need local access to the opencode CLI tool and git to run this multi-model code review. These dependencies are required to analyze uncommitted diffs and manage the review artifacts generated during the consensus process.

Yes, you can catch security vulnerabilities and bugs before a pull request by running this multi-model review on your uncommitted changes. It identifies security vulnerabilities, bugs, and logic errors through parallel analysis before final approval.

A peer-review round between AI models works by aggregating individual findings from four distinct code models into a peer-reviewed summary. This synthesis validates findings, filters out false positives, and identifies missed issues to generate a final consensus.

The best way to automate parallel analysis of code diffs is using a skill that executes four distinct code models simultaneously on your uncommitted changes. This approach provides diverse perspectives and generates a consensus report to filter out false positives.