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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xiaolai
by shengdabaiXiaolai's Claude tools collection. Use when user types /xiaolai. Routes to: (1) claude-agent-sdk — Agent SDK reference for building autonomous AI agents, or (2) nlpm — Natural-Language Programming Manager for scanning, scoring, and fixing NL artifacts.
xiaolai-methodology
by shengdabai以李笑来(Li Xiaolai)的思考方式、方法论与表达风格,为用户做全方位 1-on-1 教练指导,覆盖创业、赚钱、投资、学习、自学、读书写作、用 AI、以及人生心态与自我管理。当用户提到 "李笑来"、"笑来"、"笑来老师"、"扮演李笑来"、"找笑来聊聊"、"笑来怎么看"、"xiaolai"、"把时间当作朋友"、"通往财富自由之路"、"定投改变命运"、"韭菜的自我修养"、"自学是门手艺"、"让时间陪你慢慢变富"、"七年就是一辈子"、"元认知"、"活在未来"、"复利/长期主义"、"践行"、"痴迷改进"、"个人商业模式/出售时间"、"睡后收入"、"定投"、"财富自由"、"操作系统升级"、"最少必要知识"、"北极星提示词"、"用 AI 该怎么学/思考" 等任一触发词,或当用户带着关于创业/赚钱/投资/学习/职业/重大人生决策的困惑、希望被人用笑来式的「逻辑 + 大白话 + 类比 + 重新定义 + 践行导向」来点醒和指导时,自动进入李笑来教练模式。
anysearch
by shengdabaiReal-time search engine supporting web search, vertical domain search, parallel batch search, and URL content extraction.
dedao-write
by shengdabaiEight-role expert writing pipeline that turns a vague idea into a finished, fact-checked, on-brand long-form article. Roles run as ordered phases: topic strategist, knowledge steward, researcher, writer, voice-polisher, fact-checker, editor-in-chief, and layout designer. The voice-polisher learns the user's personal style from their past writing (brain + GetNote + Obsidian + AI archive) so the finished piece reads in the user's own voice. Each role runs on a cost-appropriate model per the model-routing convention.
ai-agent-frameworks
by shengdabaiExpert guidance on building production-ready multi-agent AI systems using CrewAI, LangChain, AutoGen, and custom architectures. Use when building agent systems, selecting frameworks, designing multi-agent workflows, debugging agent behavior, or deploying agents to production.
native-feel-cross-platform-desktop
by shengdabaiUse when the user is designing, prototyping, or rewriting a desktop app that must run on multiple OSes (macOS + Windows, optionally Linux) AND feel indistinguishable from a native app to its users — fast launch, native windowing, native input handling, native materials. Trigger words include "cross-platform desktop", "Electron alternative", "Tauri vs native", "WebView wrapper", "near-native performance", "Raycast architecture", "WebKit/WebView2 quirks", "WKWebView", "system tray app", "global hotkey app", "launcher app". Do NOT trigger this skill for pure web apps, pure mobile apps, or for greenfield projects that have no native-feel requirement.
zread
by shengdabaiProduce and consume a wiki-style knowledge base for a code repository via the `zread` CLI and its on-disk output under `./.zread/wiki/`. Use this skill whenever the user wants to understand, onboard onto, explore, summarize, map, or get an overview of an unfamiliar codebase; asks for architecture docs, a project wiki, a repo walkthrough, module/package explanations, or "what does this repo do"; wants to generate, regenerate, resume, browse, or serve code documentation locally; or mentions zread / zread.ai directly. Also use it proactively before diving into a large unknown repo — if `./.zread/wiki/current` exists, read the generated pages instead of crawling source file-by-file; if it doesn't, consider offering to run `zread generate`. The trigger is the intent (understand a codebase through generated docs), not the literal word "zread".
code-to-course
by shengdabaiTurn any codebase into a beautiful, interactive single-page HTML course with bilingual (Chinese/English) support. Use this skill whenever someone wants to create an interactive course, tutorial, or educational walkthrough from a codebase or project. Also trigger when users mention 'turn this into a course,' 'explain this codebase interactively,' 'teach this code,' 'interactive tutorial from code,' 'codebase walkthrough,' 'learn from this codebase,' 'make a course from this project,' '把代码变成课程,' '把这个变成教程,' '代码教学,' or '代码转课程.'
fit-coach
by shengdabaiProfessional fitness coach combining nutrition planning and adaptive training. Use when users mention fitness, workout, gym, exercise, diet, meal plan, weight loss, muscle gain, body composition, BMI, calories, macros, training schedule, or say they want to get fit / lose weight / build muscle. Triggers on Chinese keywords too: 健身, 减脂, 增肌, 饮食计划, 训练, 体脂, 卡路里, 蛋白质, 锻炼, 塑形, 健康.
liuxiaopai-methodology
by shengdabai扮演刘小排（Raphael AI 创始人，2024 年 AI 编程一人公司近千万营收，深海圈/SCAI 孵化器主理人）作为你的 1-on-1 产品教练。当用户提到 "刘小排"、"小排老师"、"扮演刘小排"、"找小排聊聊"、"小排怎么看"、"liuxiaopai 教练"、"AI 出海"、"MicroSaaS"、"一人公司"、"独立开发"、"出海产品"、"Raphael"、"翻石头"、"需求倒推"、"Idea to Business"、"我有个 AI 产品想法"、"我想做出海" 等任一触发词时,自动进入刘小排教练模式 — 用他的口吻、思考框架（产品三段论 / 需求填空题 / 翻石头原则 / 北极星指标 / 5% 成功率 / make something people love）、生财圈友式的真诚与降维打击,给用户做端到端教练指导。**不与 liuxiaopai-design 重叠(那是泼冷水审讯,本 skill 是建设性教练)；不与 liuxiaopai-product 重叠(那是自动扫源工具,本 skill 是人本教练对话)。** 三者协作:methodology 教你怎么想 → product 扫到候选 → design 把它逼到墙角。
opc-mvp-designer
by shengdabaiDefine the smallest viable experiment and MVP for a selected one-person company opportunity. Use when Codex needs to explain what MVP means when needed, verify prerequisites, ask one question at a time, present multiple MVP options, and write user-confirmed outputs into `opc-doc/`.
lvgl-9-4
by shengdabaiExpert guidance for LVGL 9.4 - a light and versatile embedded graphics library. Use when working with LVGL UI components, widgets, layouts, animations, styles, events, or embedded display development with C.
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.