Skill UI

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.

This comprehensive checklist guides users through the process of elevating CodeRabbit from an optional AI review tool to a mandatory merge gate in a production environment. It covers configuration auditing, technical validation (YAML syntax, review coverage), team onboarding, and setting up branch protection rules to ensure a smooth transition to required status.

This skill provides comprehensive patterns and scripts for managing rate limits when automating workflows involving CodeRabbit and the GitHub API. It covers both CodeRabbit's internal review limits and GitHub's core API limits, including best practices for safe pagination, GraphQL usage, and local caching to ensure robust, reliable automation.

This comprehensive template provides a production-grade reference architecture for CodeRabbit AI code reviews. It is designed to guide teams in establishing rigorous coding standards by defining specific, best-practice instructions for various layers (API, Database, Authentication, Frontend, CI/CD, Infrastructure). Use this as a robust starting point to build a comprehensive, automated, and secure code quality review process.

This pattern demonstrates how to automate workflows around CodeRabbit reviews. By utilizing GitHub API, TypeScript, and Bash scripting, users can programmatically fetch review results, extract detailed line-level comments, trigger specific CodeRabbit actions (like 'summary' or 'resolve'), and implement CI/CD gates to enforce quality standards before merging.

This skill provides a comprehensive guide to configuring CodeRabbit for advanced, security-focused code reviews. It teaches how to implement rules for detecting hardcoded secrets, common vulnerabilities (like SQL injection and XSS), and enforcing secure coding standards across APIs, databases, and infrastructure code. Use this when hardening a codebase's security posture or ensuring compliance with industry standards.

This guide helps users manage their CodeRabbit setup, covering plan upgrades (e.g., Free to Pro/Enterprise) and migrating configuration files. It details how to update the .coderabbit.yaml schema to adopt the latest features, advanced workflows, and best practices for enhanced code review automation.

This comprehensive guide details how to implement robust webhook endpoints for CodeRabbit, an AI-powered code review tool. It covers essential security practices, including signature validation (HMAC-SHA256), and provides structured methods for handling various event types (e.g., PR reviews, check run completions, comments). Use this skill when integrating CodeRabbit into CI/CD pipelines to automate notifications, track metrics, and enforce quality gates.

Automates Codereadr tasks via Composio’s Rube MCP by guiding tool discovery, connection checks, and multi-tool execution while enforcing schema compliance and session reuse.

This skill provides professional, AI-powered code review, integrated with Codex AI. It is designed to ensure high code quality before commits, automatically generate comprehensive CHANGELOGs, and assist in managing large-scale refactoring projects efficiently.

Behavioral rules for LLMs writing or reviewing code, emphasizing caution, simplicity, surgical edits, and goal-driven execution to avoid common mistakes during implementation tasks, bug fixes, or refactors.

Provides universal coding standards and best practices for TypeScript, JavaScript, React, and Node.js projects, covering naming, immutability, async handling, component design, API structure, and validation to keep teams aligned.