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...
x-article-publisher
by wshuyiPublish Markdown articles to X (Twitter) Articles editor with proper formatting. Use when user wants to publish a Markdown file/URL to X Articles, or mentions "publish to X", "post article to Twitter", "X article", or wants help with X Premium article publishing. Handles cover image upload and converts Markdown to rich text automatically.
deep-research
by wshuyi深度调研方法论（8步法）：将模糊主题转化为高质量调研报告。 触发词：/deep-research、深度调研、帮我调研、调研一下、对比分析 注意：如果用户需要的是可视化图谱而非报告，请使用 research-to-diagram skill。
remotion-video
by wshuyi使用 Remotion 框架以编程方式创建视频。Remotion 让你用 React 组件定义视频内容，支持动画、字幕、音乐可视化等。 触发词： - "用代码做视频"、"编程视频"、"React 视频" - "Remotion"、"remotion" - "/remotion-video" 适用场景： - 程序化视频：(1) 批量生成 (2) 数据驱动（如年度总结）(3) 音乐可视化 (4) 自动字幕 - 教程讲解视频：(5) 技术概念可视化（如 CNN、算法）(6) 分层递进讲解 (7) AI 配音教程 - 3D 视频：(8) 产品展示/模型动画 (9) 卡通角色讲解 (10) 3D 数据可视化 (11) Logo 动画
research-to-diagram
by wshuyi深度调研主题并自动生成知识关系图谱PDF。接收研究主题后自动进行网络调研、信息收集、知识整理，最终生成专业的可视化关系图谱。适用于"研究...并做图"、"深度分析...并可视化"、"生成知识图谱"等场景。
zettel-builder
by wshuyi自底向上生长的 Zettelkasten 卡片笔记系统。从笔记/已发表文章/收藏素材持续消化为原子卡, 用户价值观重述,自动链接,周期性巡检文章就绪簇,缺口处可调 deep-research 补卡。 与"自顶向下选题"或"长文写作 Skill"不重叠:那些是"等到要写时再生产",本 skill 是"素材进来就长卡"。 触发词: - "/zettel"、"/zettel-builder" - "卡片笔记"、"zettelkasten"、"原子卡"、"二级大脑" - "巡检卡片"、"长出来一篇"、"卡片就绪了吗" - "把这段切卡"、"做成卡片" 四种模式(由用户调用语义路由): - ingest:把指定素材(文本/路径/URL/本会话片段)切原子卡 → 价值观重述 → 候选链接 - scan:消化 _queue/ 里 mechanical scan 积累的 llm-wiki 新增素材 - inspect:巡检 cards/,找文章就绪簇 / 孤儿卡 / 缺口 - write:把指定簇移交给你自己的写作 Skill 出稿;过程中检测到证据缺口可调 deep-research 补卡 不做的事: - 不自动发文(成熟簇只发 Telegram 建议,等用户点头) - 不替代"自顶向下选题"型 Skill(本 skill 是自底向上长卡) - 不重写写作风格规则(在 `references/voice-snapshot.md` 维护你的价值观/文风快照,长文出稿交给你自己的写作 Skill)
translate-pdf
by wshuyiTranslate PDF documents to any language while preserving original structure, layout, and styling (colors, backgrounds, positions). Use when user wants to: (1) translate a PDF to another language, (2) convert PDF from one language to another, (3) create translated version of PDF document. Triggers: "translate PDF", "PDF翻译", "把PDF翻译成", "translate this PDF to Chinese/English/Japanese", "翻译成中文/英文"
skill-publisher
by wshuyiPublish Claude Code Skills to GitHub with proper structure, privacy checks, and bilingual documentation. Use when user wants to "publish a skill", "share a skill", "release a skill to GitHub", or asks about skill distribution.
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.