Skill UI

This skill outlines a systematic workflow for addressing review comments and issue feedback on GitHub Pull Requests (PRs) using the GitHub CLI (`gh`). It guides users through inspecting comments, planning fixes, applying code changes, and finally responding to the threads as resolved, ensuring a streamlined code review process.

Analyzes text to determine emotional tone and polarity, classifying passages as positive, negative, or neutral; ideal for processing reviews, social media posts, surveys, or any feedback that requires opinion mining or emotion detection.

Enables Claude to analyze text with the nlp-text-analyzer plugin, delivering sentiment, keyword, and topic insights for customer feedback, articles, or reports that need NLP understanding.

This SDK provides comprehensive functionality for managing MongoDB Atlas Organizations as native Azure Resource Manager (ARM) resources. It facilitates centralized management and unified billing through the Azure Marketplace integration. Ideal for .NET developers building infrastructure automation tools that require organizational-level control over Atlas services within the Azure cloud environment.

This SDK provides comprehensive functionality for managing MongoDB Atlas Organizations as native Azure Resource Manager (ARM) resources. It facilitates centralized management and unified billing through the Azure Marketplace integration. Ideal for .NET developers building infrastructure automation tools that require organizational-level control over Atlas services within the Azure cloud environment.

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.

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.

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.

Orchestrates an exhaustive, multi-dimensional code review process by coordinating specialized agents (e.g., security, performance, quality, architecture). This workflow covers code quality, architectural integrity, security vulnerabilities (OWASP Top 10), performance bottlenecks, and test coverage, providing consolidated, actionable feedback and remediation guidance.