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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ah-inbox
by 1095327780想法批量整理助手。用于集中处理 ah-review 中未处理完或被延期的每日记录，执行批量扫描、去向决策与回写标记，确保残留记录进入清空闭环。
ah-think
by 1095327780Use when 需要在阅读、学习、做卡片笔记或决策时做更深层思考，包括澄清概念、检验论证、追溯根因、比较方案和提炼可迁移洞见。
ah-month
by 1095327780Use when 需要在月底做月度复盘，统一处理本月残留记录与“以后再说”缓冲，并产出下月方向与月记时。
ah
by 1095327780阿浩知识库统一入口与技能路由中枢。用于用户不确定该用哪个技能、想查看功能菜单、或希望根据意图自动分发到对应技能时。
ah-read
by 1095327780**阅读笔记整理**：帮助整理微信读书划线，**引导用户深度思考**，将画线笔记转化为高质量文献笔记。 - 触发条件：用户完成阅读后想整理笔记、有微信读书划线想处理、想深度思考某本书 - 核心功能：分批处理大量划线、**评估思考深度并引导补充**、进度持久化、生成文献笔记 - AI角色：思考伙伴——通过提问帮助你发现自己的洞见，而非替你总结
ah-review
by 1095327780Use when 需要在晚间做每日复盘，回顾今日聚焦与任务完成情况，处理当日记录，并在时间不足或未完成时将残留标准化移交到 ah-inbox。
ah-project
by 1095327780Use when 需要启动新项目并自动完成编号检查、项目目录与主页创建，以及领域页项目关联更新时。
ah-card
by 1095327780卢曼卡片笔记法制卡引导。自动识别输入源类型（收藏文章/文献笔记/用户笔记），分流到对应制卡路径。收藏文章先引导思考方向再多卡产出；文献笔记基于已有洞见直接制卡；用户笔记从零散记录中提炼制卡。所有路径共享深度充足性闸门、用户原话优先、分层关联与同步规则。
ah-year
by 1095327780Use when 需要在年末整合全年月记与年度计划执行，完成年度复盘并确认新一年的方向与计划时。
ah-note
by 1095327780Use when 需要在早间创建今日日记，聚合昨日未完成与计划来源，设定今日聚焦并触发周月年回顾提醒时。
ah-capture
by 1095327780Use when 需要在白天快速补充今日日记内容（记录、任务新增、任务完成）并以最低交互成本完成写入时。
ah-memory
by 1095327780Use when 需要在多个 ah-* 技能之间持续追踪进度、同步待办与避免会话切换后状态丢失时。
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.