Skill UI

This expert analyzes and refactors provided code based on clean code principles, SOLID design patterns, and modern software engineering best practices. It is designed to significantly improve the quality, maintainability, and performance of complex, messy, or poorly structured codebases, ensuring the resulting code is robust, scalable, and easy to maintain.

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 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 Coinmarketcap operations through Composio's toolkit via the Rube MCP interface, enforcing tool discovery, connection checks, schema compliance, and structured execution before running tasks.

This skill generates commit messages strictly following Sentry's conventions and the Conventional Commit format. It mandates a structured commit message including a type, scope, subject, body (explaining what and why), and a footer for issue references. Use this whenever committing code changes to ensure a highly readable, traceable, and standardized version history.

This skill automatically generates comprehensive and highly structured Pull Request (PR) descriptions directly from Git diffs. It goes beyond simple summaries by automatically adding detailed change categories (e.g., source, test, config), security risk assessments, and required review checklists based on the modified file types. Use this when submitting complex code changes or when the diff is large and requires explicit structure for thorough review.

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.

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.

This skill provides a structured, git-aware mechanism to revert changes associated with specific logical work units (tracks, phases, or tasks). It performs comprehensive pre-flight checks, identifies relevant commits through structured logging, displays a detailed execution plan, and executes the revert safely in reverse chronological order. Ideal for undoing complex, multi-step development changes while maintaining project integrity.

Assists ML Training workflows by automatically activating on confusion matrix generator requests, offering structured guidance, best-practice checks, and ready-to-use code snippets.

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.

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.