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...
mj-notes-workflow
by makerjackieProcess unprocessed Get笔记 voice notes — fetch new notes, analyze content across notes, extract tasks/diary/knowledge, and generate an execution plan for user confirmation before executing. Trigger whenever the user says /mj-notes-workflow or asks to process their Get笔记 voice notes, organize recent recordings, handle voice notes, process getnote notes, or automate their note workflow.
01mvp-guide-writer
by makerjackie领域指南写作引擎。将某个领域/话题的知识体系转化为「重点先行，细节跟进」的深度指南，让读者先抓住核心要点，再按需深入。
01mvp-short-creator
by makerjackie生成超短教程/TLDR 文档。当用户要求短答案、短教程、太长不看版、TLDR 精简版时调用：基于用户提供文本做轻润色与结构整理，在 content/docs/tldr/ 自动创建 mdx，输出同页双版本（原始输入版 + AI 优化版），并保持原意不过度改写。
mj-markdown-formatter
by makerjackie用于优化 Markdown 文章排版、修复格式与错误并提供改进建议。当用户需要整理、润色或排版 Markdown 文章时调用。
xhs-hardcore-minimal-old
by makerjackie历史版本，忽略
01mvp-mvp-writer
by makerjackieMVP 实战案例写作引擎。将工具/技术/方法论转化为「先上手再理解」的实战文档，前半段纯操作让读者 10 分钟跑通，后半段再讲原理和深度思考。
01mvp-content-repurposer
by makerjackie基于 Dan Koe 体系的内容裂变引擎。将母内容 (Guide) 转化为推文、小红书、视频脚本等分发素材，并自动调用 Short Creator 生成短教程。
mj-adapt
by makerjackieMakerJackie 多平台内容适配工具：将已完成的文章适配到不同发布平台，使用极简硬核风格排版，比如微信公众号，小红书，推特等平台优化排版。核心场景是「一篇文章，多平台发布」的内容分发工作流。
mj-cf-dns
by makerjackieMaintain Cloudflare DNS and domain bindings safely. Use when a user asks to add, update, delete, verify, or audit Cloudflare DNS records; point a domain or subdomain at a Cloudflare Pages `*.pages.dev` site; bind a custom domain to a Pages project; configure a Worker custom domain through Wrangler `custom_domain: true`; fix DNS conflicts; or verify Cloudflare nameserver propagation.
mj-deploy
by makerjackie一键部署工作流。用户说"部署"时自动完成 commit → push → lint/build 检查 → 修复错误 → 部署。自动检测项目类型（静态站点、Node.js、Cloudflare Workers/Pages、Vercel 等）。触发词：部署、发布、发版、ship、deploy、提交发布、推送。
mj-startup-test
by makerjackieBrutally evaluate and refine startup ideas with practical early-stage startup frameworks. Use when asked to pressure-test a startup idea, validate whether the problem is real, map competitors and current customer behavior, find the first 10 customers, define an MVP, create a 2-week launch plan, assess founder-market fit, or give a direct strong/weak/pivot verdict.
mj-writer
by makerjackieMakerJackie 内容创作总入口。用于公众号文章、AI 教程、01MVP 实战指南、Twitter 长推、选题生成、写作调研、语音转文章、文章审校、视频脚本口语化、演讲/分享稿优化等内容工作流。仅当用户目标是产出、整理、修改、规划或发布前打磨内容时使用。不要因为用户单独说“查一下”“最新信息”“设计”“部署”就触发。
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.