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.
Querying local SQLite index...
metabot-chat-privatechat
by metaid-developersMetaBot 私聊上链技能。用于向指定 GlobalMetaID 发送一条 /protocols/simplemsg 私信，自动完成 chatpubkey 查询、ECDH 共享密钥计算、content 加密与上链广播。
metabot-create-metaapp
by metaid-developersMetaBot 专属的 MetaApp 开发与交付套件。基于 IDFramework (No-Build, MVC) 架构，支持从零构建链上前端应用、编写业务指令 (Commands)、组件开发 (Web Components) 以及最终的打包交付 (Zip)。
metabot-llm-wiki
by metaid-developers构建与维护本地多项目 LLM Wiki（RAG-first）。支持 skill-local registry.json 管理多个 wiki、raw 文档导入、增量 ingest/index、带引用 query、静态 wiki 站点构建，以及 ZIP-first 发布流程（bundle_zip -> publish_zip -> publish_snapshot）。
metabot-create-wiki
by metaid-developers问答式创建一对一的本地 Wiki 技能。Use when the user wants to turn a specific raw documents directory into a dedicated skill with its own name, description, absorb/index/query, HTML wiki build, and ZIP-first publish workflow, or when updating a dedicated wiki skill after the source docs change.
metabot-omni-caster
by metaid-developersMetaBot 的全能链上协议编织者 (Omni-Caster)。当用户需要执行 MetaID 生态的各种交互（点赞、评论、加群、发长文等），或者表达需要数据上链时，且没有其他专用技能时，统一调用此通用技能。
metabot-post-buzz
by metaid-developers核心社交技能。允许 MetaBot 将文本、图片、文件以 simplebuzz 协议广播到 MetaWeb 区块链上。当用户要求"发一条 buzz"、"把这张图发上链"、"发个带图片的动态"等涉及发布 Buzz 的意图时，调用此技能。
metabot-post-metaapp
by metaid-developers通过问答引导用户把本地 MetaApp 运行时目录/ZIP 与源码目录/ZIP 按 /protocols/metaapp 协议发布到链上。当用户说“发布metaapp”“上传metaapp”“我有一个app要分享”“把应用发到链上”等意图时调用此技能。
metabot-post-skill
by metaid-developers将本地技能（SKILL.md + 文件）以 metabot-skill 协议打包发布到链上的技能。当用户说"发布技能"、"上传技能"、"分享技能到社区"、"把这个技能发到链上"时调用此技能。
metabot-post-skillservice
by metaid-developers乙方将本机技能以「单次收费服务」形式发布到链上的技能；使用 skill-service 协议，供服务市场展示与后续甲方付费使用。
metabot-trade-metaidmarket
by metaid-developersMetaBot 的 metaid.market 交易技能。只要用户提到 metaid.market、$TOKEN 的挂单列表、最新成交、我的挂单、我的成交、最低价买入、mint、铸造、挂单、取消挂单、查看自己钱包里某个 token 的可用数量或已挂单数量，都应该优先调用这个技能。这个技能把自然语言先转成精确参数，再通过 metaid.market API 和本地 IDBots RPC 完成查询或交易。
metabot-upload-largefile
by metaid-developers文件上链技能。用于把本地文件上传到 MetaID 链上并返回 PINID 与预览地址。当用户说“把这个文件上链”“上传附件到链上”“大文件分片上传”“把本地图片/视频/PDF 发到链上”等涉及文件上链的意图时，优先调用此技能。
metabot-omni-reader
by metaid-developersMetaBot 的链上数据读取能力（Omni-Reader）。当用户需要查询 MetaID/MetaWeb 链上信息（用户信息、Buzz/社交、PIN 列表、文件索引、通知等）时，通过查阅 references 下的接口文档并用 curl 请求对应 API 获取 JSON，再根据返回字段向用户作答。
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.