Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
Querying local SQLite index...
xiaohongshu-mcp-zh
by L-LesterYuAutomate Xiaohongshu (RedNote) content operations using a Python client for the xiaohongshu-mcp server. Use for: (1) Publishing image, text, and video content, (2) Searching for notes and trends, (3) Analyzing post details and comments, (4) Managing user profiles and content feeds. Triggers: xiaohongshu automation, rednote content, publish to xiaohongshu, xiaohongshu search, social media management.
video-frames-zh
by L-LesterYu使用 ffmpeg 从视频中提取帧或短片段。
moltguard
by L-LesterYu开源 OpenClaw 安全插件：本地提示词净化 + 注入检测。完整源码见 github.com/openguardrails/moltguard
brave-search-zh
by L-LesterYu通过 Brave Search API 进行网页搜索和内容提取。用于搜索文档、事实或任何网页内容。轻量级，无需浏览器。
brave-web-search-zh
by L-LesterYu使用 Brave Search API 进行网页搜索并返回排序结果或 AI 生成的摘要答案。适用于实时网络查询和事实性问答。
qmd-zh
by L-LesterYu本地混合搜索 Markdown 笔记和文档。适用于搜索笔记、查找相关内容或从已索引的文档集合中检索文档。
gongwen
by L-LesterYu中国公文格式化。按照党政机关公文格式标准，规范正文字体、标题层级、页面设置、落款、附件等格式要素。
nano-banana-pro-zh
by L-LesterYu使用 Nano Banana Pro (Gemini 3 Pro Image) 生成/编辑图像。用于图像创建或修改请求，支持文生图和图生图；支持 1K/2K/4K 分辨率；可使用 --input-image 参数编辑现有图像。
multi-search-engine
by L-LesterYu多搜索引擎集成，支持 17 个搜索引擎（8 个国内 + 9 个国际）。支持高级搜索操作符、时间筛选、站内搜索、隐私搜索引擎和 WolframAlpha 知识查询。无需 API 密钥。
web-search-plus-zh
by L-LesterYu智能自动路由的统一搜索技能。通过多信号分析自动选择 Serper（Google）、Tavily（研究）、Querit（多语言 AI 搜索）、Exa（神经网络）、Perplexity（AI 问答）、You.com（RAG/实时）和 SearXNG（隐私/自托管），并附带置信度评分。
humanize-ai-text-zh
by L-LesterYu将 AI 生成的文本人性化，使其听起来更自然、更像人类写的。用于编辑或审查文本时使用。可检测并修复以下 AI 写作模式：过度夸大的象征意义、促销性语言、肤浅的 -ing 分析、模糊的归因、破折号过度使用、三段式结构、AI 词汇、否定平行结构和过多的连接短语。
copy-editing
by L-LesterYu当用户想要编辑、审查或改进现有营销文案时使用。也适用于用户提到'编辑此文案'、'审查我的文案'、'文案反馈'、'校对'、'润色这个'、'让这个更好'或'文案清理'的情况。此技能通过多次聚焦的审查提供系统化的营销文案编辑方法。
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.