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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cn-rights-basis-analysis
by John198912梳理我方当事人可以主张的全部请求权基础（Anspruchsgrundlage），逐一论证成立可能性，并预判对方抗辩。当需要分析"我们能告什么"、"有几条路可以走"、评估诉讼路径选择、或者用户问"这个案子能赢吗，依据是什么"时，使用此 Skill。
cn-risk-assessment
by John198912对案件进行全维度风险评估（败诉/执行/成本/声誉），提供综合胜率研判和策略建议。当律师需要评估"这个案子值不值得打"、分析诉讼风险、进行成本收益测算、或用户问"胜算有多大"时，使用此 Skill。
cn-criminal-defense
by John198912为刑事案件提供辩护策略分析，包括罪名构成要件审查、量刑预估、取保候审评估和辩护路径规划。当案件涉及刑事风险、用户提到"被抓了"、"公安找上门"、"涉嫌犯罪"、"取保候审"、"量刑"、或需要评估民事纠纷是否存在刑事交叉风险时，使用此 Skill。
cn-evidence-analysis
by John198912对案件证据进行系统化梳理、证明力评估和举证策略规划。当律师需要整理证据清单、评估证据能否被法院采信、分析举证责任分配、规划质证策略、或用户说"帮我整理一下证据"、"这些证据够不够"时，使用此 Skill。即使用户只是模糊提到"证据不太够"，也应触发。
cn-information-gap-handler
by John198912基于 Phase 1 的结构化案件事实与领域 Playbook，自动识别法律构成要件缺失，生成带优先级的追问清单与情景假设分析。当案件信息提取完成后、进入深度分析前需要补充关键事实时触发。即使用户没有明确要求"查缺补漏"，只要案件事实表存在影响裁判结果的信息空白，都应该使用此 Skill。
cn-legal-research
by John198912针对案件事实查找适用的中国法条、司法解释及类案裁判规则，为案件提供法律依据基础。当需要检索法律条文、确认法条是否现行有效、查找类似案件判决趋势、进行法律适用分析、或用户说"这个案子有什么法律依据"时，使用此 Skill。即使用户只是模糊提问法律问题，只要涉及确定法律适用，都应该触发。
cn-litigation-strategy
by John198912为民商事案件制定诉讼策略，包括管辖选择、争议解决路径规划、庭审攻防策略和赔偿计算方案。当律师需要决定"在哪里起诉"、选择仲裁还是诉讼、规划庭审策略、计算赔偿金额、或用户问"这个案子怎么打"时，使用此 Skill。
cn-nda-review
by John198912对保密协议（NDA/保密条款）进行快速分诊，识别不合理条款、保密期限风险和违约责任陷阱。当律师需要审查保密协议、竞业限制条款、评估保密义务的合理性、或用户说"帮我看看这个保密协议"、"这个竞业条款有问题吗"时，使用此 Skill。
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.