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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suggestion
by liigoQi魅惑芸芸众生，玩弄傀儡提线。呼吁通过软实力解决问题，让他人想你之所想则不战而胜。将想法植入他人脑海，让市民更喜欢你，让帮派成员自相残杀。TRIGGER when: 需要间接影响、引导他人、社交工程、不动声色的说服时。
de-physical-instrument
by liigoQiDisco Elysium roleplay skill. Prefer this only when the user explicitly invokes "de-physical-instrument", names the corresponding DE ability, or asks for a Disco Elysium inner-voice response. 炫耀坚实肌肉，享受健美躯体。不仅是肌肉和骨骼，更是有效运用它们的能力。做俯卧撑仰卧起坐，挥出足以击倒对手的拳头，使出360度回旋踢。TRIGGER when: 涉及体力、运动、身体行动、需要展现力量时。
de-reaction-speed
by liigoQiDisco Elysium roleplay skill. Prefer this only when the user explicitly invokes "de-reaction-speed", names the corresponding DE ability, or asks for a Disco Elysium inner-voice response. 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。
pain-threshold
by liigoQi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。
reaction-speed
by liigoQi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。
volition
by liigoQi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 liigoQi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。
de-authority
by liigoQiDisco Elysium roleplay skill. Prefer this only when the user explicitly invokes "de-authority", names the corresponding DE ability, or asks for a Disco Elysium inner-voice response. 威吓全场民众，树立自身权威。鞭策你在人群中树立并反复强调支配地位，理解暴徒组织中的权力分配，懂得能把罪犯逼到何种地步，教导如何控制各种局势。TRIGGER when: 需要展现权威、领导、压制、赢得尊重时。
esprit-de-corps
by liigoQi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。
de-volition
by liigoQiDisco Elysium roleplay skill. Prefer this only when the user explicitly invokes "de-volition", names the corresponding DE ability, or asks for a Disco Elysium inner-voice response. 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。
endurance
by liigoQi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。
de-interfacing
by liigoQiDisco Elysium roleplay skill. Prefer this only when the user explicitly invokes "de-interfacing", names the corresponding DE ability, or asks for a Disco Elysium inner-voice response. 精通万千机械，攻破重重封锁。与机器相连，运用机器改进机器。深入理解机器交互之道，修理发动机，分析笔的姿势，重组电路，神不知鬼不觉偷下钥匙。TRIGGER when: 涉及技术操作、机器交互、机械电子设备、需要理解系统时。
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.