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...
note-slides
by gainubi把访谈、播客、公众号长文、课程稿或复盘材料整理成横向翻页的 HTML 笔记幻灯片。默认保留材料原顺序，追踪问题、判断、例子、数字和概念原词，直接交付单文件 note slides。
xiaoyuzhou-download
by gainubiDownload Xiaoyuzhou FM podcast episode audio files from xiaoyuzhoufm.com/episode links. Use when the user provides a Xiaoyuzhou episode URL or says in Chinese or English that they want to download this podcast, download this Xiaoyuzhou podcast, download this episode, download the audio, save the podcast audio, get the .m4a file, extract the audio URL, or verify a Xiaoyuzhou audio download. Chinese trigger phrases include 帮我下载这个播客, 下载这个播客, 下载这个小宇宙播客, 下载这期小宇宙, 把这期播客音频下下来, 把这个音频保存下来.
wechat-title-generator
by gainubi公众号标题生成与评估技能。用于基于已确认的选题、大纲和目标读者，生成 8 个标题候选，筛掉低质标题，并推荐 1 个最值得发布的标题。适用于写作前或写作后定题阶段，不负责正文。
wechat-topic-outline-planner
by gainubi公众号选题与大纲策划技能。用于把一个粗点子、资料包、语音底稿或采访纪要，转成 2-3 个高价值选题角度、1 个推荐方向、1 套主大纲和 1 套备选大纲。适用于写作前的方案阶段，必须先确认结构，再交给写稿技能。
article-illustrator
by gainubi为文章生成可截图的扁平化解释配图 HTML 页面。用于用户提供文章、草稿、段落或文件路径，并要求配图、文章插图、图解、公众号配图、生成 illustration 页面、帮我配图。输出是本地 HTML 文件，不直接生成 PNG/JPG，不用于 Logo、图标、海报、封面或非文章内容。
thesis-word-formatter
by gainubi大学生毕业论文 Word 排版技能。用于先收集学校 Word 模板、学院规范、任务书或示例论文，再对本科或硕士毕业论文进行 Word 版式整理、目录与编号校正、参考文献与图表题注检查，并输出可直接复核的排版执行清单。适用于用户说“给毕业论文排版”“按学校要求整理 Word”“检查论文格式”“论文目录标题编号不对”“把这篇论文整理成提交版”等场景。
wechat-draft-writer
by gainubi公众号初稿写作技能。用于在选题和大纲已确认后，基于参考资料、语音底稿和文风 DNA 生成一版高保真初稿。适用于正文写作阶段，不负责选题、大纲和标题决策。
wechat-style-profiler
by gainubi面向公众号作者的文风 DNA 梳理技能。用于从 3-10 篇参考文章中建立可复用的风格画像，输出 14 维分析、标点符号偏好、分块习惯、段落配方、叙述方法体系、内容推进方式和默认 DNA 文件，并通过用户校准确保画像可直接给写稿技能调用。
storyboard-video-previs
by gainubiGenerate cinematic storyboard plans and image generation prompts from a story, theme, script, music video idea, commercial concept, or visual brief. Use when Codex needs to turn narrative text into a panel by panel storyboard, shot list, animatic plan, video previs prompt, image model prompt, or bilingual Chinese and English storyboard prompt with camera language, motion notes, annotation systems, continuity cues, and visual style constraints.
apple-calendar-scheduler
by gainubiParse natural-language schedule requests into Apple Calendar events, especially Chinese phrases such as "明早 8 点开会" or multiple events in one sentence. Use when Codex needs to extract event details, check Apple Calendar conflicts before writing, warn about overlaps, and create events with a default 30-minute reminder on macOS.
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.