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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cold-start-interview
by zhou210712冷启动访谈——建立你的监管监测清单、索引政策库并了解你的重要度阈值，使监测器输出信号而非噪音。适用于全新安装、重新配置时（--redo），或重新检查哪些连接器实际响应时（--check-integrations）。
bar-prep-questions
by zhou210712法考备考题目——客观题或主观题，针对你的薄弱科目和考试类型。追踪错题并回归 薄弱模式。当用户说"法考练习""客观题""主观题""测试我"时使用。
cold-call-prep
by zhou210712课堂提问准备——预测老师可能提问的问题并以苏格拉底式追问训练，标注你的薄弱 环节以便课前重温。当用户说"准备明天的课""课堂提问[案例]""[老师]可能在 [案例]上问什么"或指向指定阅读材料时使用。
cold-start-interview
by zhou210712关于你的访谈和材料收录——课程、法考报考地、学习风格（追问训练型 vs 讲解引导型）、 过往大纲、有反馈的批改论文、历年考题、法考真题集、教学大纲、已撰写论文。 在新安装、用户说"设置"或"开始"时使用，或使用 --check-integrations 重新探测连接器。
customize
by zhou210712引导式自定义你的法学学习画像——无需重新运行完整的新手导入访谈即可修改单项设置。 调整当前课程、学习风格、大纲偏好、法考备考科目、种子材料或学习节奏。 当用户说"修改我的[某项]""添加课程""更新我的画像""新学期""自定义"时使用。
exam-forecast
by zhou210712分析同一授课教师的历年考题以揭示模式——科目权重、反复出现的考点陷阱、 偏好的案例假设类型、政策vs法条分析的比例——并预测今年考试可能的重点。 当用户说"考试考什么""分析历年考题""预测考试"或分享历年考题时使用。
flashcards
by zhou210712生成或训练法条概念记忆卡片——莱特纳式记忆桶，按科目的 Markdown 存储， 带自我评估的训练模式。当用户说"训练记忆卡片""根据[材料]制作记忆卡片" "考我卡片"或想记忆法条时使用。
irac-practice
by zhou210712给 IRAC 论文评分——结构、考点识别、规则准确性、分析深度和组织。绝不代写 论文或展示范文；追踪跨练习的模式。当用户说"批改我的 IRAC""检查我的论文" 或"我写了这个，给我反馈"时使用。
outline-builder
by zhou210712按你的格式从课堂笔记和教材构建或扩展课程知识大纲。搭建框架——不替你 写大纲。当用户说"大纲[科目]""添加到我的大纲""从[材料]构建大纲" 或指向课堂材料时使用。
socratic-drill
by zhou210712苏格拉底式追问训练——我问，你答，我追问。在你真正掌握之前绝不给你答案。 当用户说"训练我""提问我""苏格拉底式""测试我的[科目]"或想进行主动学习时使用。
study-plan
by zhou210712构建或更新长期法考备考（或期末备考）学习计划——分阶段、按薄弱科目的权重分配、 每日练习安排，根据 study-plan.yaml 中的练习历史自适应调整。 当用户说"制定学习计划""规划我的法考备考""安排我的复习""我该怎么复习[X]"时使用。
ramp
by zhou210712学生学期导入——诊所程序、工具导览、真实案件之前的实践练习。 读取指导老师在设置时上传的手册并以互动方式教学。 当新诊所学生说"帮我导入""我是诊所新人""开始"，或每学期开始时使用； 传入 --card 获取一页参考卡。
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.