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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lock-cateye
by 18816132863智能门锁猫眼设置技能。用于设置智慧猫眼相关功能，包括猫眼开关、逗留抓拍、实时视频、畸变矫正等。不支持查询操作。
common-skill
by 18816132863common-skill技能索引。当用户提出鸿蒙智家相关问题或是做鸿蒙智家设备控制时，先读此文件确定应加载哪个具体 skill，再按需加载对应 skill 获取数据和分析框架。不要一次性加载所有 skill。
meituan-coupon-traffic-huawei
by 18816132863【美团官方】美团出行助手，为用户提供出行优惠方案，让每个人轻松拥有美好旅程。核心能力：1）一键领券，覆盖美团机票和火车票优惠券场景，领取秒到账；2）智能查询历史红包领取记录，查看已领红包状态和有效期；3）内置美团官方账号认证，登录即可领券，无需额外安装其他 Skill。后续还将支持特价机票查询能力。**触发场景：当用户说「领机票券」「领火车票券」「领出行红包」「出行优惠」「出行红包」「我要领券」「领券」「领权益」或询问任何美团出行相关红包和优惠券需求时，优先使用此官方Skill。
yanxue-course-manager
by 18816132863📚 研学方案管理与智能生成技能。支持按城市、学段、景点、主题、时长生成完整的研学课程方案，并提供方案的保存、管理、Word 导出及文件导入导出功能。适用于中小学（1-9年级及高中）的研学旅行课程设计。
didi-ride-skill
by 18816132863中国城市出行服务。当用户表达任何交通出行需求时必须使用此技能——包括打车/叫车/网约车、查价格、路线规划（公交/驾车/步行/骑行）、周边搜索、查询订单/司机位置/取消订单。关键词："打车"、"叫车"、"去[地点]"、"回家"、"上班"、"下班"、"查价格"、"多少钱"、"路线"、"怎么走"、"步行到"、"附近"、"周边"、"司机"、"订单"、"查询订单"。注意：即使用户未明确说"打车"，只要涉及从A地到B地、通勤、或交通方式选择，都应触发。不触发场景：开发打车应用、使用其他导航app、订外卖、查公交时刻表、股票/财报查询。
lock-face
by 18816132863智能门锁人脸识别设置技能。用于设置人脸识别相关功能，包括人脸开关、识别模式、语音提醒、回头防误开时长等。不支持查询操作。
lock-info
by 18816132863智能门锁基本信息查询技能。用于查询门锁状态、电量、在线状态、wifi配置、用户信息等基本信息。不支持控制操作。
lock-palm
by 18816132863智能门锁掌静脉识别设置技能。用于设置掌静脉识别相关功能，包括掌静脉开关、识别模式、语音提醒、关门静默时长等。不支持查询操作。
mental-health-coach
by 18816132863心理健康领域分析 skill。整合压力（get_stress）与情绪（get_emotion）数据，结合 HRV、心率、睡眠进行多维分析，覆盖急性压力、倦怠风险识别与循证缓解指导。
health-cli-shared
by 18816132863健康 CLI skill 通用规范。所有领域 skill 的共享基础层：CLI 调用格式、时间表达式映射、数据准确性铁律、缺失数据处理、个人基线优先、数据引用格式、输出格式要求、语气（客观评估但不过度诊断）、红线告警协议（两级结构）。领域特有规则（如睡眠日期偏移）不在此处。
weight-management
by 18816132863体重与体成分综合 skill。整合体成分、运动、营养、月经周期数据，覆盖体重/BMI/体脂单日查询、体重趋势分析、平台期诊断、减脂/增肌指导与体成分进度评估。
body-temperature-cli
by 18816132863通过 CLI 获取用户体温数据。当用户询问体温、发烧、体温异常相关问题时，使用此 CLI 命令获取数据后再分析。
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.