Skill UI

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.

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.

Guides how to author Claude Code slash commands using Markdown prompts, YAML frontmatter, dynamic arguments, Bash execution, and best-practice organization for reusable workflows and interactive automation.

This skill converts vague coding requests into a highly structured, actionable, and atomic development plan. It guides the user through defining the approach, scope (In/Out), and breaking down the task into a mandatory checklist of ordered, concrete steps (e.g., discovery, implementation, validation). Ideal for ensuring comprehensive and traceable project development workflows.

Automates conditional request helper workflows for API development, offering guidance, best practices, and production-ready code/configurations for REST, GraphQL, OpenAPI, and authentication patterns.

This skill guides the execution of development tasks defined in a structured implementation plan, strictly following the Test-Driven Development (TDD) workflow. It manages the entire development lifecycle, including pre-flight checks, context loading, task identification, writing failing tests, implementing minimal code, refactoring, committing changes, and performing phase-wise verification. Ideal for managing complex, structured software development projects.

A comprehensive guide for treating project context as a managed, first-class artifact alongside code. It establishes systematic documentation (e.g., product vision, tech stack, workflow) to ensure consistent requirements, maintain team alignment, and optimize AI interactions throughout the entire development lifecycle. Essential for greenfield and brownfield projects.

An expert system for designing and implementing advanced AI context architectures. It specializes in integrating intelligent memory systems, vector databases, and knowledge graphs to ensure AI applications maintain coherent, relevant, and scalable state across complex, multi-agent workflows. Essential for enterprise RAG implementation and stateful AI development.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

This skill unlocks advanced multi-step code generation and architectural refactoring capabilities using autonomous agents. It supports parallel agent workflows, complex multi-file migrations, and multi-codebase orchestration by autonomously calling tools (Read, Write, Bash) in controlled, checkpointed cycles, drastically improving large-scale development efficiency.

A comprehensive guide detailing how to optimize the entire software development lifecycle within the Cursor IDE. It walks users through an AI-augmented loop covering planning (Chat), scaffolding (Composer), implementing (Tab), refining (Inline Edit), testing (Chat/Terminal), committing, and reviewing code using integrated AI and Git features. Designed for maximizing developer efficiency and code quality.