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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composing-videos
by yikartCombines multiple videos/images into a single video with optional background audio. Use when user wants to merge clips, concatenate videos, create slideshow from images, stitch videos together, combine media files, add background music to video, mix video with audio, create video montage, or join multiple video segments. 合并视频、拼接视频、图片合成视频、添加背景音乐、视频拼接、多图生成视频、视频混剪、素材合成。
editing-videos
by yikartVideo editing using Volcengine Track structure. Supports cutting, trimming, adding text, stickers, audio, filters, effects, transitions, multi-clip compositions, speed adjustment, watermark removal. 视频剪辑、裁剪视频、添加文字、添加水印、添加音频、视频滤镜、视频特效、视频转场、多片段拼接、调整速度、去水印。
extracting-thumbnails
by yikartExtracts thumbnails from video URLs. Use when publishing video content that requires a cover image, when the user does not provide a cover/thumbnail, or before publishing to platforms that require cover images (Kwai, Bilibili, YouTube). 提取封面、视频封面、生成封面、缩略图提取、视频缩略图、截取封面。
editing-images
by yikartImage editing using Sharp. Supports compositing (QR codes, logos, watermarks), resizing, cropping, rotating, flipping, brightness/contrast/saturation adjustment, blur, sharpen. 图片编辑、图片合成、添加二维码、添加Logo、添加水印、图片缩放、图片裁剪、图片旋转、图片翻转、亮度对比度饱和度调整、模糊、锐化。
generating-drama-recaps
by yikartGenerates narrated recap videos from short drama clips using AI-powered narration and voice synthesis. Use when user wants to create drama summary, narrated recap, voice-over video, drama commentary, drama explanation video, short drama recap, drama montage with narration. 短剧解说、短剧二创、戏剧解说、影视解说、视频解说、配音解说、AI解说视频、剧情解说、短视频解说、短剧混剪解说。
generating-videos
by yikartGenerates videos using Grok (preferred) and Google Veo 3.1 models. Supports text-to-video, image-to-video, first-last-frame, video extension, and reference images. AI视频生成、文生视频、图生视频、首尾帧生成、视频拓展。
sync-service-docs
by yikart为指定服务维护产品文档与代码的同步。文档不存在时以 OpenAPI 规范为基础从头生成；文档已存在时根据 git diff 更新受影响章节，并刷新最后更新时间和代码基准 hash。
transferring-video-styles
by yikartConverts live-action videos into artistic styles like comic, anime, manga, 3D cartoon. Use when user wants to transform video style, create anime version, apply artistic effects, convert to cartoon style, make comic video, create manga style video, stylize video. 视频风格转换、动漫风格、漫画风格、卡通风格、视频二次元化、视频风格化、AI风格转换、视频转动漫、视频转漫画。
translating-videos
by yikartTranslates videos including subtitles, voice cloning, and lip sync. Use when user wants to translate video, dub video to another language, convert video language, localize video content, create multilingual video, add translated voiceover, change video audio language, or produce foreign language version. 视频翻译、配音翻译、字幕翻译、视频本地化、多语言视频、翻译视频、视频配音、语言转换。
analyzing-videos
by yikartAnalyzes video content and extracts highlights. Use when user wants to analyze video, extract highlights, create video summary, generate video keywords, understand video content, find best moments, create trailer, extract exciting clips, get video insights, or identify viral moments. 视频分析、提取精彩片段、视频摘要、视频理解、精彩集锦、视频关键词、剪辑精华、内容分析、热门片段。
crawling-social-media
by yikartDownloads and extracts content from social media platforms. Use when user wants to crawl, download, or extract videos/content from Bilibili, YouTube, TikTok, Douyin, Instagram, Twitter, Xiaohongshu. 下载视频、抓取视频、爬取内容、提取视频、下载B站视频、下载抖音视频、下载YouTube视频、视频采集。
generating-images
by yikartGenerates images using AI models (Gemini). AI图像生成、文生图、生成图片。
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.