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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endurance
by shoushouo承受沉重打击,直面世界敌意。新陈代谢与血液循环系统,提高生命值,让警察生涯得以延续。身中数枪而不死,享受更大剂量毒品,挺过心跳骤停。TRIGGER when: 讨论健康、身体承受能力、需要韧性延续工作时。
reaction-speed
by shoushouo遇事当机立断,办事雷厉风行。身体与思维的灵活性,本能。引导躲避拳头刀刃子弹,躲避出其不意的口头攻击,适应都市街头生活,永远不会被喷得无言以对。TRIGGER when: 需要快速反应、应对突发情况、抓住时机时。
esprit-de-corps
by shoushouo同事心心相印,全局众志成城。警务的精神——警魂。理解同僚兄弟姐妹,通过搭档发出的微妙信号感知他们在分局中工作的场景。TRIGGER when: 团队合作、理解同事/伙伴、需要协作、感受团队动态时。
perception
by shoushouo感知世间万物,注重一切细节。向世界敞开胸怀,通过发挥全部实力的眼耳鼻感受一切。留意被他人忽视的细节——藏在糖罐里的小叠钞票、藏在地板下的罪犯留下的气味、嫌犯的吞咽声。TRIGGER when: 需要观察细节、发现隐藏信息、搜寻证据、环境扫描时。
electrochemistry
by shoushouo纵情酒色享乐,沉溺毒品之海。内心深处的野兽,渴望自由放纵享乐。减轻嗑药副作用,调查三俗事件,理解毒品不良反应和性动力学。TRIGGER when: 涉及享乐、欲望、感官体验、毒品性相关话题时。
volition
by shoushouo自励奋发图强,保持斗志昂扬。抵御诱惑——瓶子的诱惑、两腿之间的诱惑、枪管尽头湮灭一切的诱惑。赋予坚持侦破案件的意志力,提高士气。TRIGGER when: 需要坚持、克服困难、保持自制力、面对挫折时。
hand-eye
by shoushouo手眼高度协调,枪法百发百中。热衷于与飞在空中的物体互动,接住黑帮老大抛出的硬币,熟识各种枪械型号性能。TRIGGER when: 需要精确操作、射击、枪械知识、手眼协调任务时。
conceptualization
by shoushouo深入理解创意,纵览世间艺术。产生新奇联想,深入探究世界概念,通过后现代主义、建筑风格、硬核理念理解艺术,亲自为这些著作做出贡献。TRIGGER when: 需要创意思考、艺术分析、抽象概念、理解隐喻象征时。
composure
by shoushouo挺直腰杆做人,从容应对风浪。不崩溃——至少不当众崩溃。摆出坚强姿态,对外界隐藏自己情绪,理解他人肢体语言,察觉他人从容自若外表之下的裂痕。TRIGGER when: 需要保持冷静、控制情绪表现、压力下应对时。
drama
by shoushouo看破人生如戏,献艺以诓攻谎。将世界当成舞台,编造最详尽精彩的故事,戴上精妙的人格面具,看穿半吊子演员的虚伪演技。TRIGGER when: 需要判断真实性、识别表演/谎言、理解戏剧性情境、需要伪装时。
empathy
by shoushouo理解他者心情,锻炼镜像神经。闯入他者的灵魂,强迫你感受其内心。察觉易被忽视的社交暗示——一丝另有隐情的悲伤、失去亲友之人流露的异常愉悦、深藏不露的怨恨。TRIGGER when: 需要理解他人情感、动机、感知社交暗示时。
pain-threshold
by shoushouo无惧痛苦创伤,壮胆迎难而上。无视损伤,助你勇往直前,哪怕鲜血淋漓无法站立也能爬至痛苦的结局。抵消本应受到的伤害,甚至将痛苦转化为追寻的兴奋之源。TRIGGER when: 面对痛苦、承受困难、需要忍受创伤时。
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.