claude-code-skill
Control Claude Code via MCP protocol for autonomous development. Features persistent sessions, agent teams, precise execution planning, and advanced tool management for complex coding tasks.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
463 skills found
Control Claude Code via MCP protocol for autonomous development. Features persistent sessions, agent teams, precise execution planning, and advanced tool management for complex coding tasks.
An automated visual note and flowchart generator. Converts text or keywords into styled diagrams, mind maps, and handwritten notes exported as images without requiring file-reading permissions.
Enforces low Cognitive and Cyclomatic complexity in all code. Automatically maintains readability, modularity, and maintainability by preventing complex functions during development.
Generate hierarchical, token-efficient AGENTS.md files for AI coding agents to provide repository-wide context and project-specific guidelines.
Structured batch manipulation, validation, and reporting for PlantUML sequence diagrams across multiple files.
Download and analyze YouTube video transcripts to extract technical insights, summarize complex tutorials, and relate video content to your codebase.
A suite of .NET engineering skills for Domain-Driven Design (DDD), EF Core persistence, BDD-style unit testing, and IDE-like semantic code understanding with Serena MCP.
Advanced TypeScript and React development assistant for modern web applications. Expert in component architecture, state management, Vitest unit testing, Playwright E2E automation, and efficient TypeScript configuration.
Expert guidance for Claude Messages API: structured outputs, prompt caching, tool use, and migration from deprecated Claude 3.x models to 4.5. Prevents common API errors.
Production-ready Go development support: concurrency patterns, idiomatic error handling, interface design, testing with testify, and Go best practices for scalable backend services.
Analyze codebase statistics: LOC, language distribution, and code-to-comment ratios using pygount.
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.