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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polymarket-knowledge
by cyl19970726Polymarket CLOB API knowledge base for order management, WebSocket events, and trading operations. Use when working with order lifecycle (place, fill, cancel), debugging WebSocket USER_TRADE/USER_ORDER events, understanding API field mappings, or implementing trading logic.
guide-meta-agent-system
by cyl19970726v5 入口 — Lead + Sub-agent + Agent-Team-Member + Meta-meta + Skill-manager + Advisor 协作系统宪法（7 角色）。 v4 在 SKILL.md 单文件中累积过满（1638 行），v5 拆分为 13 个聚焦文件 + 1 入口索引， 提升 cold-start navigability，并新增 7 角色 (含 skill-manager + advisor) / mermaid 数据流图 / enforcement layer / dashboard 规范。
guide-meta-thinking
by cyl19970726元思考（MetaThink）方法论：从第一性原理推导系统行为，而非机械查文档。 核心是理解"为什么这样设计"而非"它是什么"，从设计约束推导出不可消除的行为边界， 用这些边界作为代码正确性的 eval 标准，并区分"我们的 bug"与"系统的固有限制"。 有以下意图时必须加载此 skill： (1) 不知道某个行为是 bug 还是系统正常行为 → 用元思考做 fault attribution (2) 面对新的技术系统，需要快速建立正确的心智模型 → 用元思考推导约束 (3) 对一段代码有多种实现方式，不知道哪种正确 → 用元思考找 eval 标准 (4) reconcile 发现异常，不确定根因 → 用元思考的约束链追溯 (5) 需要理解为什么 guide-polymarket-fundamentals 这样设计 → 读本 skill
guide-agent-teams
by cyl19970726Claude Code Agent Teams 完整使用指南：多 session 协作、共享任务、inter-agent 通信。 包含 Lead 管理方法论（Spawn 设计原则、协调模式、纠正策略、知识提取时机）。 使用场景：(1) 创建和管理 agent team (2) 设计 spawn prompt 和角色分工 (3) 理解 tools API（TeamCreate/SendMessage/Task*/EnterPlanMode） (4) 排查 agent teams 问题（teammate 不出现、权限、shutdown） (5) 对比 subagents vs agent teams 选型 (6) Lead 准备创建 agent team 前参考 spawn prompt 设计清单 (7) 协调并行 agents 时选择合适的协调模式 (8) Agent 偏离任务时应用 3-level 纠正策略 (9) 决定何时提取知识到 guide-*/observe-* skills
lead-daily-practice
by cyl19970726metal-coder 项目内 Lead 的日常操作 SOP —— 把元思考定义 + meta-cli 11 命令 + Tower 蒸馏组合成可执行的 7 步流程。 必须加载场景： (1) 在 metal-coder 项目（/home/hhh0x/metal-coder/）内推进任何工作 (2) 不知道某个场景该调哪个 meta-cli 命令 (3) 写完一个 meta 文件，需要自检是否合格元思考 (4) chain 累积 ≥5 metas，考虑是否 Tower distill (5) 决定是否 fork 一个工作为多个 sub-chain 本 skill 是元思考定义（docs/meta-thinking/）和 meta-cli 命令实现的 bridge 层。 没有它，Lead 会 default 手写 metas 而不调 cli，工具不被使用价值流失。
skill-creator
by cyl19970726Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. Also use for Earning Engine PM v2 skill creation — task-* skills (development tasks), strategy-* skills (trading strategy lifecycle), observe-* skills (analysis reports), guide-*/map-* skills (knowledge docs). When users say "create a task", "plan a new feature", "analyze architecture", "write a guide", or "develop a new strategy", use this skill to route to the appropriate PM v2 template.
guide-metal-coder-essence
by cyl19970726metal-coder 的北极星定义 —— 8 个元思考组成的演化序列，构成项目本质。 从"我们解决什么问题"开始，到"系统怎么自己进化"结束， 每个元思考都 derives_from 前一个，本身就是一条 derivation chain。 **所有其他 skill / cli 命令 / docs 都是这 8 个元思考的具体化或优化。** 读完本 skill 才算真正理解 metal-coder。 必须加载场景： (1) 第一次接触 metal-coder（在 docs/ 之前先读本 skill） (2) 设计任何新功能 / 新命令前 — 先确认它服务哪个元思考 (3) 评估"这个改动是不是核心"— 用 8 元思考序列对照 (4) 想理解 metal-coder 与 v5 / docs/ / lead-daily-practice 的关系 (5) 跨 work chain 反思时 — 检查 8 元思考是否真的在被实施
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.