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).
Guide for creating and integrating MCP (Model Context Protocol) servers with Claude Code. Use when building external tool integrations, connecting to databases, creating custom capabilities, or adding API wrappers. Covers transports, OAuth, configuration, and troubleshooting.
Creates plugins (skills and MCPs) by guiding through the authoring process. Use when users want to create or update (1) skills that extend Claude's capabilities with specialized knowledge and workflows, or (2) MCPs (Model Context Protocol servers) that provide tools for Claude Desktop.
基于 RAG 的知识库检索技能。从本地文档中检索相关信息，支持语义搜索和精确匹配。适用于问答、信息查询、文档理解等场景。
Execute complex multi-tool MCP workflows directly using TypeScript or Python. As the MCP executor agent, you have MCP servers configured and write code that composes multiple tool calls. Use for 3+ MCP tool calls, complex data processing, parallel operations, or retry logic. This skill is colony-aware - you execute code directly via Bash without subagents.
Research agent for external documentation, best practices, and library APIs via MCP tools
Understand where FAF works - Claude Code (CLI + Skills), claude-faf-mcp (Desktop + MCP), vs claude.ai (web - file upload only). Explains faf-cli vs MCP differences, when to use each platform. Use when user asks "does FAF work with", "CLI vs MCP", "Claude Desktop vs claude.ai", or platform compatibility questions.
Skill 與 Agent 建立指導專家，協助開發者依據 GitHub Copilot 最佳實踐建立高品質的 custom agent 和 skill 定義檔案，包含 YAML frontmatter、MCP 整合與互動式工作流程設計。
This skill should be used when building AI agents using prompt-native architecture where features are defined in prompts, not code. Use it when creating autonomous agents, designing MCP servers, implementing self-modifying systems, or adopting the "trust the agent's intelligence" philosophy.
Analyze, compare, and work with tokenizers using Unsloth tools. Compare different tokenizers, analyze token efficiency, and integrate with Unsloth models. For SuperBPE training, see the 'superbpe' skill.
Enable AI-assisted development (vibe modeling) within Domino using MCP (Model Context Protocol) servers. AI coding assistants like Cursor and GitHub Copilot can execute commands as Domino jobs, maintaining security, governance, and reproducibility. Use when setting up AI code assistants to work with Domino, configuring MCP servers, or enabling vibe modeling workflows.
Auto-save research progress using sage_autosave_check MCP tool. INVOKE WHEN: user asks to research, compare, analyze, or investigate topics; after completing web searches; when synthesizing conclusions ("therefore", "in summary"). INVOKE FOR: checkpoint, save this, remember this, save my research. DO NOT INVOKE: for simple Q&A, code editing, or file operations unrelated to research.
Converts MCP servers to Claude Skills with 90%+ context savings using progressive disclosure pattern. Supports automatic detection and on-demand conversion.
Manages MCP (Model Context Protocol) servers for Claude Code and Claude Desktop. Activates for: finding MCP servers, MCP registries, MCP server setup, MCP configuration, MCP troubleshooting, MCP security, building MCP servers, MCP transport selection, claude mcp commands, mcp.json files, mcpServers configuration, tool poisoning prevention, MCP debugging, Smithery, Glama, GitHub MCP registry, which MCP server should I use. NOT for: general Claude Code settings (use claude-code-mastery), OAuth/authentication unrelated to MCP, generic API integrations without MCP.
Work with Cloudflare Workers, Workers AI, edge functions, and serverless deployment. Use when deploying to Cloudflare, implementing edge functions, running AI inference at the edge, or working with MCP servers on Cloudflare.
Expert in .faf (Foundational AI-context Format) files for persistent project context. Use when working with .faf files, project DNA, CLAUDE.md bi-sync, faf-cli commands, MCP server configuration, or AI-readiness scoring (0-100%). Updated for v3.3.7.
Structured reasoning for complex problems using Sequential Thinking MCP. Break down problems into stages, track thought progression, identify connections, and generate insights. Use for architectural decisions, complex debugging, research planning, or any problem requiring deep analysis.
Build production-ready MCP clients in TypeScript or Python. Handles connection lifecycle, transport abstraction, tool orchestration, security, and error handling. Use for integrating LLM applications with MCP servers.
Build headless automation and agentic workflows with Google's Gemini CLI. Covers approval modes (default, auto_edit, yolo), file permission model, Edit vs WriteFile tool selection, smartEdit configuration, GEMINI.md context files, settings.json hierarchy, and MCP server integration. Use when building CI/CD pipelines with Gemini, debugging "0 occurrences found" edit failures, configuring --approval-mode for automation, creating long-running agents with --resume, or integrating external services via Model Context Protocol.
This skill should be used when the user asks to "create MCP server", "add MCP tools", "implement FastMCP endpoints", "expose MCP resources", "configure MCP servers", or mentions Model Context Protocol, tool schemas, or MCP integration patterns.
