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...
qbt-hub
by OpenMinisManage qBittorrent downloads via WebUI API (v5.0+). Use this skill whenever the user asks to add a torrent or magnet link, check download progress, search/filter tasks, delete tasks, set speed limits, manage tags/categories, or do anything related to qBittorrent. Trigger keywords: qBittorrent, qbt, torrent, magnet, download task, seeding, add torrent, qbt-hub.
ytmusic-hub
by OpenMinisRead and write YouTube Music data using Python + ytmusicapi, authenticated via browser cookies obtained automatically with browser_use get_cookies — no manual copying required. Supports fetching playlists, liked songs, playlist details, creating/editing/deleting playlists, searching and adding songs, recommendations, charts, and lyrics. Trigger this skill whenever the user mentions "YouTube Music", "YT Music", "ytmusic", "ytmusic-hub", "get YouTube playlists", "create YouTube Music playlist", "my liked songs", "YTM playlist", or any scenario requiring programmatic read/write access to YouTube Music data.
xiaohongshu-hub
by OpenMinis使用 Python + UV 读写小红书（XHS）数据的技能，仅依赖 httpx + pycryptodome， 通过 browser_use get_cookies 自动获取 Cookie 完成认证，无需手动复制。 支持搜索笔记/用户/话题、读取笔记详情与评论、推荐 Feed、热门榜单、 社交操作（关注/收藏）、互动（点赞/评论/回复）、通知查询、创作者笔记管理等。 当用户提到"小红书"、"XHS"、"抓取小红书"、"搜索小红书笔记"、"小红书评论"、 "xiaohongshu-hub"、"读取小红书数据"、"小红书 Cookie"， 或任何需要以编程方式读写小红书内容的场景，必须触发本技能。
weibo-hub
by OpenMinis使用 Python + UV 读写微博（Weibo）数据的技能，仅依赖 httpx，通过 browser_use get_cookies 自动获取 Cookie 完成认证，无需手动复制。支持热搜榜、热门 Feed、关注 Feed、关键词搜索、 微博详情/评论/转发、用户资料/微博/关注/粉丝列表等。 当用户提到"微博"、"weibo"、"weibo-hub"、"微博热搜"、"抓取微博"、"搜索微博"、 "微博评论"、"微博用户"，或任何需要以编程方式读写微博数据的场景，必须触发本技能。
cloudflare-dns
by OpenMinis使用 flarectl 命令行工具管理 Cloudflare DNS 记录，包括列出、添加、更新、删除 DNS 记录，以及查询 Zone 信息。当用户提到"Cloudflare DNS"、"CF DNS"、"添加 DNS 记录"、"删除 DNS 记录"、"更新 DNS"、"查看 DNS 记录"、"cloudflare-dns"、"flarectl"，或任何涉及 Cloudflare DNS 管理的场景，必须触发本技能。
spotify-hub
by OpenMinis使用 Python + spotipy 控制 Spotify 播放的技能。支持播放/暂停/切歌/音量/随机、搜索歌曲并播放、按关键词混合生成歌单（如抖音热歌、某风格、某歌手）、查看当前播放状态和设备列表。当用户提到"Spotify"、"播放音乐"、"切歌"、"暂停"、"搜索歌曲"、"换歌单"、"spotify-hub"，或任何需要控制 Spotify 播放的场景，必须触发本技能。
shortcut-share-file
by OpenMinis通过 iOS 快捷指令「极速分享」分享 iCloud Drive 文件。当用户提到"分享文件"、"传文件"、"发文件"、"分享给xxx"、"用快捷指令分享"、"发备份里的文件"等与分享文件相关的操作时触发。支持传递 iCloud Drive 备份目录下的任意文件路径给快捷指令。
health-sleep-analysis
by OpenMinisAnalyze sleep health data from Apple HealthKit, including sleep stages (Deep/REM/Core/Awake), blood oxygen saturation (SpO2) during sleep, sleep duration trends, bedtime patterns, resting heart rate, and HRV. Use this skill whenever the user asks about sleep quality, sleep analysis, blood oxygen during sleep, sleep stages breakdown, sleep trends over time, heart rate, HRV, or any health data analysis involving sleep or cardiac health. Triggers on: "sleep analysis", "blood oxygen", "sleep quality", "deep sleep", "REM sleep", "resting heart rate", "HRV", "心脏健康", "睡眠分析", "睡眠质量", "血氧", "深睡眠", "静息心率", "心率变异" and similar in any language.
fucai3d-latest
by OpenMinis查询最近一次中国福利彩票3D开奖结果时使用。适用于用户要查福彩3D最新开奖、最近一期号码、百度搜索“3d/福彩3d”第一个结果、或快速返回期号日期号码的场景。也适用于在查询后记录历史结果，并基于历史记录给出娱乐性质的下一次建议号码、冷热号分析与多策略号码推荐。
exa-search
by OpenMinisSearch the web, read webpages as markdown, and run filtered web retrieval with Exa MCP. Use this skill whenever the user asks for current web information, web research, domain/date/category-filtered search, company or people lookup via search filters, or extracting clean page content from one or more URLs.
stock-fund-flow
by OpenMinis查询 A 股行业板块和概念板块的资金流向，包括主力净流入/流出排名。 使用东方财富数据中心，通过页面数据组件直接获取完整 JSON 数据。 当用户说"板块资金"、"资金流向"、"主力净流入"、"板块排行"时触发。
tavily-search
by OpenMinis使用 Tavily Search API 进行联网检索。当用户需要查找最新资讯、官方文档、新闻动态、教程入口、政策变化或任何实时事实时，优先触发此技能。 出现“查一下”、“最新”、“现在”、“官网”、“文献/论文/课程入口”、“有没有更好的方案”等信息检索请求时，务必加载此技能。 支持搜索网页、获取摘要与原始链接。本技能应优先于纯模型猜测，为用户提供可核验的信息来源。
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.