OpenAI Codex CLI configuration guide for Claude. Use when users ask about: codex config, ~/.codex/config.toml, codex sandbox policies, codex approval modes, creating codex skills, codex mcp servers, codex model settings, local LLM providers, or integrating tools with Codex.
Efficiently compose multiple MCP tool operations by launching a subagent that writes and executes TypeScript or Python code. Use when you need to compose 3+ MCP tool calls, process their results together, implement complex logic, or avoid token overhead from loading many MCP tool schemas into main context. The subagent has MCP servers configured and writes code that calls tools dynamically.
Always use Context7 MCP to fetch up-to-date library/API documentation before generating code that depends on external libraries or config. Use when the user asks for setup steps, configuration, or code involving third-party libraries. Prefer Context7 docs over memory. Include citations to the retrieved docs when possible.
Token-efficient context gathering and synthesis from multiple sources (memory, docs, web). Orchestrates MCP tools to build comprehensive context before analysis or interviews. Use when starting discovery, research, or analysis tasks.
Guide for building high-quality Model Context Protocol (MCP) servers that allow LLMs to interact with external services. Use when creating new MCP integrations, tools, or servers for Claude or other AI systems.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
This skill should be used when the user asks about "token efficiency", "compress responses", "reduce token usage", "minimize context", "compact format", "token optimization", or discusses reducing token consumption in MCP responses while maintaining value.
Build autonomous AI agents using Claude Agent SDK with computer use, tool calling, MCP integration, and production best practices
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Expert guidance for using the Pal MCP Server (zen-pal-nas). This skill should be used when working with multi-model AI orchestration, tool workflows (chat, thinkdeep, planner, consensus, debug, codereview, precommit, clink), configuration troubleshooting, or optimizing model selection strategies. Activates automatically when user mentions Pal MCP, zen-pal-nas, or specific tool names.
Build autonomous AI agents using Claude Agent SDK with computer use, tool calling, MCP integration, and production best practices
Build AI agents with the Strands Agents SDK, an open-source framework from AWS. Use when building autonomous agents, creating custom tools with @tool decorator, orchestrating multi-agent systems (swarm, graph, agents-as-tools), integrating MCP servers, or deploying agents to production. Supports Amazon Bedrock, Anthropic API, OpenAI, Ollama, and other model providers.
Build MCP servers for integrating external APIs with AI agents
This skill should be used when the user asks about "JSON flags", "token IDs", "cross-tool references", "progressive detail", "response optimization", "human and LLM readable", "automation flags", "confidence-based detail", or discusses optimizing MCP responses for both human and machine consumption. Provides patterns for human/LLM readable responses with automation-friendly structures.
Creates Model Context Protocol servers with tools, resources, and prompts for AI assistant integration. Use when extending Claude with custom capabilities, building AI tool integrations, or exposing APIs to AI assistants.
Model Context Protocol integration for external tools (GitHub, filesystem, SPARQL).
Expert guidance for researching, documenting, and integrating Model Context Protocol (MCP) servers and tools. Covers MCP architecture, server/client implementation patterns, tool discovery, integration workflows, security best practices, and multi-language SDK usage (Python, TypeScript, C#, Java, Rust). Enables seamless integration of MCP tools into Claude Code and AI applications.
This skill should be used when the user asks to "configure agent profile", "add skills to agent", "set up MCP servers", "configure agent tools", "write system prompt", "create agent workflow", "define agent commands", "add subagents", or needs to define what capabilities an agent has and how to orchestrate complex workflows in the runtime.
