Skill UI

Provides comprehensive guidance for developing robust, cross-platform Command Line Interface (CLI) applications. Covers the entire workflow from initial UX analysis and command design to implementation using popular frameworks (e.g., commander, click, cobra). Key features include advanced argument parsing, handling shell completions, displaying progress bars, and ensuring reliable terminal interactions across Windows, macOS, and Linux.

Provides step-by-step guidance for automating Coassemble workflows through Composio’s Rube MCP, covering tool discovery, connection checks, schema compliance, and execution best practices so integrations stay reliable.

Guide for automating Codacy tasks through Composio’s Rube MCP, highlighting connection setup, tool discovery, and execution patterns while always fetching schemas first.

Automates code of conduct documentation by guiding through generator patterns, setup instructions, and validation checks tailored for technical documentation workflows.

Automates CodeQL database builds, suite selection, and taint-aware scans to surface security and quality issues across supported languages, handling output directories, SARIF results, and extensions in one workflow.

This skill executes CodeRabbit's core workflow, providing automated, in-depth code reviews directly on pull requests. It analyzes code diffs based on custom rules (e.g., API validation, DB security), generates detailed walkthrough summaries, and posts line-level feedback. Ideal for setting up robust CI/CD quality gates and maintaining high code standards throughout the development lifecycle.

This skill guides users on fine-tuning CodeRabbit's review quality. It teaches the AI agent team preferences through PR feedback, integrates custom code guidelines (e.g., security policies), allows customization of the review tone, and implements path filters to drastically reduce false positives and noise. This ensures that all code feedback is highly relevant, actionable, and aligns with specific team standards.

This guide provides a step-by-step walkthrough for setting up automated AI code reviews using CodeRabbit. It covers creating the necessary `.coderabbit.yaml` configuration file, defining path-specific instructions, triggering reviews via Pull Requests (PRs), and interacting with the feedback using PR comments or the local CLI. Essential for integrating quality gates into CI/CD workflows.

A comprehensive runbook for handling CodeRabbit service outages or malfunctions. It provides step-by-step procedures for quick triage, emergency bypasses (P1), checking installation health (P2), diagnosing delays (P3), and tuning incorrect review behavior (P4). Essential for maintaining continuous CI/CD workflows when the review bot fails.

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.

Monitor the effectiveness and adoption of AI code reviews using CodeRabbit. This skill calculates critical metrics including review coverage percentage, time-to-first-review, and comment acceptance rates. It is ideal for building organizational dashboards to maintain high code quality and optimize development workflows.

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.