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...
openai-image-gen
by fufankejiBatch-generate images via OpenAI Images API. Random prompt sampler + `index.html` gallery.
adversarial-architecture-selection
by fufankeji把"多候选技术架构选型"打包成"法庭式 5 角色对抗调研"的标准化流程，用 Claude Code Agent Teams 让代言人/红队/集成评估师互相质疑，主 Claude 充当法官综合判决，最终产出架构基线决策文档。适用：多个开源项目 fork 选型、多个 SaaS/云厂商选型、多个技术栈对决（React vs Vue vs Svelte）、多个开源库选型（LangChain vs LlamaIndex）、多个架构方案选型（Monolith vs Microservices vs Serverless）、PRD 写到一半发现技术决策有争议需要 deep dive。即使用户没明确说"用 Skill"，只要任务沾边"多候选架构选型需要更严谨的对抗评估"都要调用。触发关键词：架构选型 / 技术选型 / fork 选型 / 候选对抗 / 多方案对比 / 选型纠结 / 架构决策 / 技术栈对决 / 帮我决定用 X 还是 Y / SaaS 选型 / 库选型 / 对抗调研 / architecture decision / tech selection / framework comparison / vendor evaluation / adversarial review。不用于：单一候选无对比（不需要对抗）、纯产品决策不涉技术（用 brainstorming）、已有强烈倾向只想确认（用 devil's advocate 单 agent Skill 即可，本 Skill 过重）。
figma-to-nextjs-migration
by fufankeji把 Figma Make / v0.dev / Bolt / Lovable 等 UI 原型生成器产出的 Vite + React Router 项目， 搬家（迁移 / 搬运 / 换成 / migrate / port）到 Next.js 15 App Router 生产脚手架。覆盖路由 迁移（react-router → next/link + next/navigation）、样式系统合并（globals.css + @theme + @custom-variant dark）、字体切换（@import Google Fonts → next/font/google）、客户端组件 标注（"use client"）、Hydration mismatch 修复（seededRandom 替换 Math.random）、shadcn/ui 组件库复用、路径别名统一（~/ → @/）。典型触发场景：用户说"把 Figma Make 项目换成 Next.js"、 "v0.dev 出的代码怎么部署"、"Vite 项目跑不了流式 API / 服务端组件 / Edge Runtime"、 "React Router 搬家到 App Router"、"Bolt / Lovable 出的原型怎么改造成能上线的项目"。 Make sure to use this skill whenever the user mentions Figma Make, v0.dev, Bolt, Lovable, Vite, React Router, Next.js, App Router, 搬家, 迁移, migrate, port, 流式 API, 服务端组件, Edge Runtime, 部署, 即使用户没有明说"请用 figma-to-nextjs-migration skill"。 不处理反向迁移（Next.js → Remix / Next.js → Vite / App Router → Pages Router）。
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.