Skill UI

A specialist tool designed to generate, format, and validate comprehensive technical documentation. It supports multiple standards, including Google/NumPy docstrings (for Python), JSDoc (for TypeScript), and full OpenAPI/Swagger specifications. Key features include automated detection of code languages and frameworks, robust validation of code examples (using tools like doctest and OpenAPI linters), and generating structured user guides or site configurations.

This comprehensive skill uses industry-standard tools like Prettier to automatically format and validate code files across multiple languages (JavaScript, TypeScript, JSON, CSS, Markdown, etc.). It ensures code style consistency, improves readability, and helps maintain a clean, professional codebase. Use it when needing to "format my code," "check formatting," or enforce code style across a project.

This expert analyzes and refactors provided code based on clean code principles, SOLID design patterns, and modern software engineering best practices. It is designed to significantly improve the quality, maintainability, and performance of complex, messy, or poorly structured codebases, ensuring the resulting code is robust, scalable, and easy to maintain.

An expert AI specialist that integrates advanced static analysis (e.g., CodeQL, SonarQube) with modern DevOps practices. It performs multi-layered code reviews, checking for security vulnerabilities, performance bottlenecks, architectural flaws, and adherence to best practices across various languages. Ideal for CI/CD pipelines and pull request validation.

This comprehensive checklist provides a systematic framework for conducting thorough code reviews. It covers critical areas including functionality, security vulnerabilities, performance optimization, and overall code maintainability. Use it when reviewing pull requests, conducting code audits, or standardizing team development practices.

This skill helps transform code reviews from simple gatekeeping into a powerful knowledge-sharing mechanism. It guides users through systematic analysis of code changes, focusing on correctness, security, performance, and maintainability. Use it when reviewing pull requests, establishing best practices, or mentoring developers to ensure high-quality, robust codebase development.

This skill simulates a senior engineer conducting comprehensive code reviews. It analyzes code diffs and files to identify bugs, security vulnerabilities (e.g., SQL injection, XSS), performance issues (N+1), code smells, and architectural flaws. It is ideal for reviewing pull requests, performing code quality audits, or conducting security assessments, providing structured, actionable, and prioritized feedback.

Automated skill providing code splitting guidance for frontend development, covering React/Vue/CSS, modern performance and accessibility best practices, and producing validated, production-ready configurations.

This expert analyzes and refactors existing codebases to enhance quality, maintainability, and performance. It specializes in applying clean code principles, SOLID design patterns, and modern software engineering best practices. Use it to clean up technical debt, simplify complex modules, or prepare a codebase for new feature development.

This skill guides the comprehensive, multi-step rollout of CodeRabbit AI code review across an entire organization. It covers setting organization-level defaults, customizing team-specific review rules (e.g., API security, frontend performance), and automating deployment via multi-repo scripts and branch protection enforcement. Use when establishing standardized, high-quality AI code review practices company-wide.

Configure CodeRabbit CLI to integrate AI-powered code reviews directly into your local development loop. This workflow allows developers to run comprehensive, AI-driven reviews against staged or unstaged changes before committing or pushing to a remote repository. It establishes a crucial 'two-layer' review process, providing instant, fast feedback at the local CLI level, complementing the formal PR review process. This tool is essential for maintaining high code quality and consistency during rapid development cycles.

This guide provides a comprehensive method for optimizing CodeRabbit's review process, focusing on improving speed, relevance, and the signal-to-noise ratio. Learn best practices for managing PR size, selecting the right review profile (e.g., 'assertive' vs. 'chill'), using path instructions to guide feedback, and filtering out low-value files. Ideal for teams looking to reduce review fatigue and ensure consistent code quality checks.