Skill UI

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.

Automates configuration reference generation within technical documentation, offering guidance, best practices, and production-ready code while validating outputs against standards to support API docs, user guides, architecture docs, and automation workflows.

Addresses the critical challenge of managing extremely long conversation histories (millions of tokens) in advanced AI agents, such as coding assistants. This technique moves beyond simple token minimization by implementing structured compression strategies (like Anchored Iterative Summarization) to prevent the loss of critical technical details—such as file paths, decisions, and error messages—ensuring the agent maintains functional context across complex, multi-step tasks. It emphasizes optimizing for 'tokens-per-task' rather than just tokens-per-request.

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.

This skill details advanced techniques for enhancing the effective context capacity of LLMs. It outlines four core strategies—Compaction, Observation Masking, KV-Cache Optimization, and Context Partitioning—to prevent context limits from constraining task complexity. By strategically managing token usage, users can reduce operational costs, minimize latency, and implement robust, long-running agent systems for handling large documents or conversations.

This skill set provides advanced, structured strategies for optimizing Large Language Model (LLM) interactions. It covers techniques such as intelligent context summarization, dynamic trimming, context routing, and precise token budget allocation to prevent 'context rot'. It is essential for building robust, long-running, multi-turn conversational AI systems.

This skill automatically fetches the latest official documentation for programming libraries and frameworks (such as React, Next.js, or Prisma) via the Context7 API. It is essential for developers who require accurate, up-to-date technical reference material to ensure their code adheres to the most current best practices.

Automatically evaluates Claude Code sessions at the Stop hook, identifies reusable patterns, and stores them as learned skills for future reuse, helping you capture troubleshooting steps, debugging techniques, and project conventions without manual effort.

Automates contributing guide creation in technical documentation; triggers on explicit requests, follows best practices, offers step-by-step help, generates code/config samples, and validates outputs for API docs, user guides, and architecture docs.

The official SDK for building applications that programmatically interact with GitHub Copilot. It provides comprehensive features including session management, custom tool definition (function calling), real-time streaming, and lifecycle hooks. Supports multiple languages, including Node.js, Python, Go, and .NET, making AI integration seamless across your tech stack.

Automates searching the web for technical insights and packing them into clear GitHub issues so teams can immediately act on findings. Trigger it when you need research translated into tracked work or security/feature tickets.

Guides CTOs and tech leaders through strategy, architecture governance, team scaling, and crisis response, with workflows for technical debt assessment, ADR creation, and vendor evaluation to keep engineering healthy.