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.
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supermemory
by gaoqiongxie跨会话持久记忆：自动捕获、压缩和检索项目上下文，比静态RAG更智能。当用户说'记住之前'、'跨会话记忆'、'长期上下文'、'session记忆'、'项目记忆'、'自动记忆'、'上下文继承'时触发。核心特点：自动加载上次会话、事实提取、时间感知、智能遗忘过期信息。
confidence-check
by gaoqiongxieAI自我置信度评估技能，对回答的可靠性和确定性进行自我评估。当用户询问AI是否确定、可靠性评估、需要评估答案可信度时触发。
relationship-patterns
by gaoqiongxie亲密关系人格识别与关系模式辨别 Skill。 帮助用户识别亲密关系中的人物性格类型、辨别不健康的关系模式（如吊桥效应、PUA、煤气灯效应、吹狗哨等）， 提供自我觉察与关系反思的工具，适用于个人成长、情感复盘、心理科普等场景。 触发词：不健康的关系、PUA、吊桥效应、煤气灯、吹狗哨、关系模式、人格识别、情感操控、toxic、讨好型、自恋型依恋。
weather-pro
by gaoqiongxie专业气象分析师：多数据源天气解读、趋势预测、行业气象建议。当用户说'分析天气'、'气象数据'、'天气预报解读'、'农业气象'、'飞行气象'、'航海气象'、'台风分析'、'寒潮预警'、'气候趋势'、'为什么下雨'、'气压变化'、'湿度影响'时触发。核心特点：整合中国气象局/OpenWeatherMap/彩云天气多源数据，提供个人/农业/航空/航海多场景专业建议，兼具数据解读、趋势预测和气象科普。
mental-health-check
by gaoqiongxie专业心理健康自评工具：PHQ-9抑郁自评/GAD-7焦虑自评/PSS压力知觉/MBI职业倦怠简易评估。当用户说'我觉得抑郁了'、'焦虑测试'、'压力好大'、'心理测试'、'情绪不好'、' burnout'、'睡不着心里烦'、'最近情绪很低落'时触发。核心特点：标准化临床量表、分级解读、求助资源、严格免责声明。
music-recommender
by gaoqiongxie音乐推荐与歌单生成：根据心情、场景、天气、活动或口味偏好推荐歌曲，生成主题歌单。当用户说'推荐首歌'、'适合跑步的音乐'、'下雨天听什么'、'咖啡时光BGM'、'睡前音乐'、'派对歌单'、'工作时听什么'、'心情低落推荐歌'、'生成一个歌单'时触发。核心特点：多维度匹配（情绪/场景/天气/年代/流派）、歌单编排逻辑（起承转合）、跨平台适配（网易云/QQ音乐/Spotify/Apple Music链接格式）、音乐知识科普（流派/背景/鉴赏）。
caveman-skill
by gaoqiongxie洞穴人Token优化：让AI用最精简的语言输出，削减高达75%的token消耗，同时保持技术准确性。当用户说'节省token'、'token太贵'、'精简输出'、'caveman模式'、'压缩输出'、'减少字数'、'用最少的词'时触发。核心特点：输出压缩、输入压缩、子技能生态、多语言变体支持。
cardiac-arrest-guide
by gaoqiongxie心脏骤停科普与急救指南：起因、预后、急救步骤、个人风险评估。当用户说'心脏骤停是什么原因'、'心肺复苏怎么做'、'猝死风险'、'心脏骤停预后'、'心梗和心脏骤停区别'、'AED怎么用'、'我父亲心脏骤停了'、'猝死的征兆'时触发。核心特点：医学知识科普+标准化急救流程+个人风险自评，面向普通人，大白话解释，强调立即拨打120。
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.