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...
vyibc-playwright-proxy-diagnosis
by ChangfengHUDiagnose Playwright/Chrome browser pages that show ERR_TUNNEL_CONNECTION_FAILED, blank pages, or cannot access websites while the normal network works. Use when a project browser instance uses BROWSER_PROXY_SERVER, system proxy, AdsPower proxy, or a persistent Chrome profile and the user suspects the browser instance or proxy is broken.
vyibc-login-status
by ChangfengHU查询抖音账号的登录状态。当用户说"查一下登录状态"、"我登录了吗"、"凭证还有效吗"、"dy_xxx 登录了没"、"检查一下抖音是否在线"时触发。
vyibc-gemini-ads-ha
by ChangfengHUGemini Ads 高可用图片生成。支持 10+ 提示词自动调度、失败重试、任务查询与取消。
vyibc-doc-to-online-page
by ChangfengHU将本地文档（Markdown/Text）发布为在线网页链接。采用工业级 `documents:toPage` 渲染引擎，生成精美、可公开分享的在线文档页面。
vyibc-common-issues
by ChangfengHU项目技术问题诊断、故障排除与最佳实践仓库。
vyibc-auto-publish
by ChangfengHU将抖音/OSS视频一键发布到抖音账号。当用户说"发布到抖音"、"上传到我的抖音"、"把这个视频发到抖音"，或者有ossUrl且要求发布时触发。
vyibc-auto-parse
by ChangfengHU解析抖音、小红书视频链接，自动去水印并上传到 OSS/Supabase/R2，返回永久可访问地址。当用户粘贴抖音或小红书分享链接、要求下载视频、去水印、存到OSS/R2时触发。
vyibc-reboot-recover
by ChangfengHU服务器重启后一键恢复运行环境（项目、VNC、AdsPower、自愈修复）。
vyibc-publish-status
by ChangfengHU查询抖音发布任务的进度和状态。当用户说"发布进度怎么样"、"查一下发布状态"、"任务id是xxx"、"发布成功了吗"、"看看发布日志"、"任务状态"时触发。
vyibc-ads-us-egress-stable
by ChangfengHU一键修复 AdsPower 美国出口（强制启动参数代理、重启分身、验IP、持久化自愈配置）。
vyibc-ai-batch-creator
by ChangfengHU高效 AI 批量出图引擎 —— 专为大规模素材产出而生。 支持基于参考图或纯文本的批量任务分发，内置并发调度与自动重试机制。 实时生成动态 HTML 预览页面，助你轻松追踪百张图级的生成进度。
vyibc-adspower-main-window
by ChangfengHU恢复并置前 AdsPower 主界面窗口，适合 AdsPower 主页面被最小化、隐藏或切到后台时使用。
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.