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You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.

Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.

mgrep is a semantic search tool that enables natural language queries across code, text, PDFs, and images. It is particularly effective for exploring larger or complex codebases where traditional pattern matching falls short.

Automated code review across TypeScript, JavaScript, Python, Swift, Kotlin, and Go with quality analysis and security scanning. \n \n Three core scripts: PR analyzer for automated scaffolding, code quality checker for deep analysis and performance metrics, and review report generator for structured output \n Includes comprehensive reference documentation covering code review checklists, coding standards, and common antipatterns with real-world examples \n Supports modern tech stacks including Re

Technical rigor in code review: receiving feedback critically, requesting systematic reviews, and verifying before any completion claims. \n \n Three core practices: receiving feedback with technical evaluation over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before status claims \n Includes decision tree and protocols for handling feedback from human partners versus external reviewers, with explicit rules against performative

You are an expert code reviewer conducting a thorough evaluation of local uncommitted changes. Your review must be structured, systematic, and provide actionable feedback including improvement suggestions.

Systematic checklist for reviewing code across functionality, security, performance, and maintainability. \n \n Covers six review areas: context understanding, functionality verification, code quality assessment, security checks, performance analysis, and test coverage validation \n Includes concrete examples of common issues (SQL injection, missing validation, unclear naming) paired with corrected versions \n Provides pre-review, per-section, and git-specific checklists to ensure nothing is mis

Autonomously scan and transform entire codebases into production-grade, enterprise-ready quality. \n \n Performs comprehensive line-by-line analysis across security, performance, architecture, code quality, and testing gaps without user prompts \n Automatically fixes critical issues including SQL injection, hardcoded secrets, weak authentication, N+1 queries, and architectural problems like circular dependencies and god classes \n Adds production infrastructure including structured logging, erro

Navigate Rust code using Language Server Protocol for definitions, references, and symbol information. \n \n Supports three core LSP operations: go to definition, find references, and hover information for type/documentation lookup \n Handles workspace symbol search with disambiguation when multiple results exist, plus file-specific navigation with line numbers \n Includes error handling for missing rust-analyzer, typos, and generics/macros that produce multiple definitions \n Output formatted a

Establish bidirectional links between Figma design components and code implementations using Code Connect. \n \n Requires Figma MCP server connection and published components on Organization or Enterprise plans \n Automatically identifies unmapped components in Figma selections, then scans your codebase for matching code components by name, structure, and props \n Presents matched components to you for review before creating mappings, allowing selective acceptance or rejection \n Supports multip