council-review

Multi-agent code review works by simulating domain expert subagents to analyze files for security, refactoring, and quality issues. It assigns files based on domain relevance and outputs prioritized findings to identify potential flaws across your project.

You can run automated TypeScript linting and vitest checks during an AI code review. The process integrates these automated quality checks as a baseline before simulating domain expert subagents to analyze your codebase for deeper security and performance issues.

To perform a comprehensive code review before merging, run this process on your project files to simulate experts analyzing security, frontend, backend, Postgres, performance, UI, UX, LLM pipeline, and test quality, ensuring adherence to best practices.

The best way to prioritize code review findings is to use a multi-agent review that outputs a consolidated list categorized by severity. It assigns files based on domain relevance, producing prioritized findings for Postgres, backend, and other specialized quality areas.

AI code review works for evaluating LLM pipeline and UX quality by leveraging a council of specialized simulated subagents. It assigns files based on domain relevance to perform deep, domain-specific analysis across these and other technical areas.