Skill UI

A protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

This specialized skill acts as an expert debugger, designed for deep root cause analysis of software errors, test failures, and unexpected behavior. It guides the user through systematic debugging processes, including capturing stack traces, isolating failure points, implementing minimal fixes, and providing comprehensive reports detailing the root cause, evidence, specific code patches, and prevention recommendations. Ideal for complex bug resolution and quality assurance.

This expert guide provides a systematic, structured methodology for debugging complex software issues. It teaches users how to analyze stack traces, correlate logs, establish consistent reproduction steps, and apply hypothesis-driven testing to isolate and resolve bugs, crashes, or unexpected behavior in any codebase. Essential for professional troubleshooting and quality assurance.

A comprehensive guide for mastering major dependency version upgrades in software development. This skill covers compatibility analysis, implementing staged upgrade strategies, resolving dependency conflicts, and setting up robust testing matrices (Unit, Integration, E2E) to ensure a smooth and reliable modernization process for any codebase.

Personalized reports that analyze recent Claude Code chats to surface recurring challenges, highlight learning gaps, curate HackerNews resources, and deliver actionable development insights directly to your Slack DMs for targeted growth.

A comprehensive set of production-ready design patterns for integrating with the Documenso SDK. It covers best practices for managing singleton clients, building typed service wrappers, robust API error handling (including status code analysis), and automatically implementing retry logic with exponential backoff. Ideal for establishing stable, scalable, and maintainable coding standards in TypeScript and Python.

This expert skill specializes in creating comprehensive, maintainable technical documentation directly from existing codebase, configurations, and comments. It can generate various artifacts, including API specifications, system architecture diagrams, detailed user guides, and technical references, ensuring consistency and accuracy using AI-powered analysis and industry best practices. Ideal for building robust documentation pipelines and standardizing documentation across large repositories.

Analyzes a codebase to surface subdomains (Core, Supporting, Generic) and recommend bounded contexts using DDD strategic design, helping teams clarify domain boundaries, cohesion, and service split decisions.

This skill acts as a comprehensive security scanner specializing in detecting Cross-Site Scripting (XSS) vulnerabilities in client-side code. It performs static analysis across React, Vue, Angular, and vanilla JavaScript, specifically identifying unsafe practices like direct HTML manipulation (innerHTML), raw rendering (v-html), and insecure URL handling. Use it when auditing frontend code for security flaws and needing best practices for secure coding.

This skill orchestrates a comprehensive, multi-stage Git development workflow, simulating the process from code commit to PR preparation. It leverages specialized AI agents (code reviewers, test automators, prompt engineers) to systematically enforce best practices. Key functions include code quality assessment, breaking change analysis, running unit/integration/E2E tests, and generating Conventional Commit messages, ensuring code readiness and high quality before merging.

Automated heap dump analysis guidance for performance testing, offering step-by-step instructions, best-practice patterns, production-ready code and configs, and validation against common standards.

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.