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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linyuebanzi-inline-diagram
by lqshow为林月半子(LQ)的技术长文自动识别插图位置,并生成 16:9 概念图/流程图/对比图/架构图。支持七种风格:手绘网格笔记本风(notebook)、专业扁平信息图(infographic)、现代高级科技商务风(executive-tech)、温暖手绘卡片风(cozy-handdrawn)、技术简笔画风(tech-doodle)、卡通信息图风(cartoon-infographic)和白板手绘风(whiteboard-sketch)。当用户需要给已经写好的文章配插图、加配图、排版优化、让长文不那么单调时使用。触发词包括:加插图、配图、加几张图、让文章更生动、排版优化、文章太单调了、给文章画几张手绘图、手绘示意图、信息图、商务风配图、科技商务风。skill 的核心价值在于"智能识别文章里哪些段落值得配图"——优先选抽象概念、流程循环、对比分类、架构组件这四类地方,不在每段机械配图。使用 MuleRun Nano Banana 2 Generation API 生成,支持通过 --style 切换不同视觉风格。不要用于封面图(走封面 skill)、截图替代、纯代码展示、表情包生成。
linyuebanzi-image-gen
by lqshow通用图像生成执行层,支持 MuleRun Nano Banana 2、APImart GPT Image 2 和 Atlas Cloud GPT Image 2 三种生图 API。通过 --provider 切换。支持 generation(纯文本生图)和 edit(带参考图修图)两种模式,单张和批量执行。这是被其他 skill 调用的基础设施 skill,不直接面向终端用户。当其他 skill(如 cover-hero、inline-diagram)需要调 API 生图时,调用本 skill 的 scripts/generate.py。不要用于:提示词撰写、风格注入、业务校验——这些由调用方 skill 负责。
neon-slides
by lqshow生成深色科技感(neon dark)风格的 HTML 幻灯片/演示页面,橙蓝对比配色,适合技术文章解读、架构讲解、产品介绍、课程大纲、直播课件等技术类内容。中文触发:「做一份深色科技风幻灯片」「把这篇文章做成 slides」「生成一个技术课件」「做个直播 deck」「把大纲变成演示页面」「dark tech 风格的幻灯片」。English triggers: "make a dark tech slide deck", "turn this outline into neon slides", "generate an HTML presentation for my tech talk", "create a landing-style slide deck". 只生成单文件 HTML(非 .pptx),支持键盘翻页、跳转、投屏直播。不要用于短图文封面、小红书配图、需要 PowerPoint 格式交付的场景。
tech-manga-explainer
by lqshow生成技术科普漫画，用对话形式解释复杂的技术概念。当用户请求「用漫画解释技术」「生成技术科普漫画」「把这个技术概念画成漫画」「漫画教程」「用漫画讲解 XXX」或类似需求时使用。适合 n8n、Kubernetes、AI、编程、架构等技术话题。通过 nanobanana + Gemini API 生成图片。
food-diorama-skill
by lqshowGenerate 3D historical gourmet diorama images for Chinese cities using Google Gemini API. Creates artistic miniature food worlds with four-quadrant layouts featuring iconic dishes, Pop Mart style figures, and cultural elements. Use when the user asks to create food diorama, 美食盲盒, gourmet blind box, city food scene, or mentions generating 3D food artwork for cities like 西安, 重庆, 成都, 北京, 广州.
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.