multi-ai-code-review

To run a parallel AI code review, execute the skill on your current branch or specify a PR. It auto-detects the base branch, gathers diffs and commits, and performs multi-perspective MCP analysis to output a structured report with findings and risk priorities.

Multi-perspective AI code review covers architecture, quality, security, testing, performance, conventions, and consistency. It runs parallel MCP servers to analyze each dimension independently before integrating the results into a single structured output.

Yes, you can use MCP servers to review code changes for security and architecture. The skill executes parallel MCP review servers targeting these specific dimensions, alongside testing and performance, to provide a comprehensive evaluation.

Automated code review detects the base branch automatically during Phase 1 collection. It then gathers diffs and commit history to prepare the review context, grounding the subsequent parallel MCP analysis in the actual code changes.

Parallel code review requires no specific external dependencies or components to function. It leverages integrated MCP servers to handle the multi-perspective analysis, architecture evaluation, and security checks independently.

The best way to integrate AI code analysis is to run it against current branch changes or specified PRs. The skill outputs a structured report with findings, recommendations, and alignment checks, which can be consumed by CI tools to enforce code health.