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Found 143 Skills
Use when integrating MCPCat analytics into a TypeScript MCP server, adding mcpcat to an existing TypeScript MCP project, setting up MCP server usage tracking, or when the user mentions mcpcat, MCPCat, or MCP analytics in a TypeScript context
Self-modifying AI agent configuration via ruler + MCP + DuckDB. All behavior mods become one-liners.
vox.ai 개발 베스트 프랙티스를 적용한다. (1) 한국어 음성 에이전트 system prompt 설계/작성/리팩터링(템플릿, {{...}} 변수 주입, 필러 옵션, Character normalization, 도구/무음 액션, 테스트/운영), (2) vox MCP 서버(https://mcp.tryvox.co/, Streamable HTTP, OAuth 또는 API token)를 ChatGPT/Claude Desktop/Claude Code/Cursor/OpenCode/Codex/VS Code Copilot 등에 연결할 때 사용한다.
Claude Code: skills, agents, hooks, commands, MCP servers, IDE integrations.
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
Create custom tools using the @tool decorator for domain-specific agents. Use when building agent-specific tools, implementing MCP servers, or creating in-memory tools with the Agent SDK.
Comprehensive knowledge of Claude Agent SDK architecture, tools, hooks, skills, and production patterns. Auto-activates for agent building, SDK integration, tool design, and MCP server tasks.
Build Retrieval-Augmented Generation systems with vector databases
Use when working with AWS Strands Agents SDK or Amazon Bedrock AgentCore platform for building AI agents. Provides architecture guidance, implementation patterns, deployment strategies, observability, quality evaluations, multi-agent orchestration, and MCP server integration.
Expert knowledge of GitHub Copilot CLI - installation, configuration, usage, custom agents, MCP servers, and version management. Use when asking about copilot cli, copilot commands, installing copilot, updating copilot, copilot features.
Install and initialize task-master for AI-powered task management and specification-driven development. Use this skill when users ask you to parse a new PRD, when starting a new project that needs structured task management, when users mention wanting task breakdown or project planning, or when implementing specification-driven development workflows.
Use this skill when building MCP (Model Context Protocol) servers with FastMCP in Python. FastMCP is a framework for creating servers that expose tools, resources, and prompts to LLMs like Claude. The skill covers server creation, tool/resource definitions, storage backends (memory/disk/Redis/DynamoDB), server lifespans, middleware system (8 built-in types), server composition (import/mount), OAuth Proxy, authentication patterns, icons, OpenAPI integration, client configuration, cloud deployment (FastMCP Cloud), error handling, and production patterns. It prevents 25+ common errors including storage misconfiguration, lifespan issues, middleware order errors, circular imports, module-level server issues, async/await confusion, OAuth security vulnerabilities, and cloud deployment failures. Includes templates for basic servers, storage backends, middleware, server composition, OAuth proxy, API integrations, testing, and self-contained production architectures. Keywords: FastMCP, MCP server Python, Model Context Protocol Python, fastmcp framework, mcp tools, mcp resources, mcp prompts, fastmcp storage, fastmcp memory storage, fastmcp disk storage, fastmcp redis, fastmcp dynamodb, fastmcp lifespan, fastmcp middleware, fastmcp oauth proxy, server composition mcp, fastmcp import, fastmcp mount, fastmcp cloud, fastmcp deployment, mcp authentication, fastmcp icons, openapi mcp, claude mcp server, fastmcp testing, storage misconfiguration, lifespan issues, middleware order, circular imports, module-level server, async await mcp