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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html-to-image
by inclusionAIHTML 转图片 skill - 将 HTML 文件或内容通过 agent-browser 渲染并截图为图片。适用于生成信息图、社交媒体配图、数据可视化截图等场景。
video-subtitles-and-audio-insert-workflow
by inclusionAIBurn hard subtitles from UTF-8 SRT files using moviepy 2.x with CJK-capable system fonts; tune font size, placement, stroke, and encode settings (bitrate or CRF) to avoid oversized outputs. Documents ffprobe/ffmpeg workflows for inspection, encoding, and batch jobs; troubleshooting for fonts, bitrate, and pacing. Covers voiceover with edge-tts (voice selection, rate/volume/pitch), matching narration length to video with atempo/apad, and multi-scene pacing with breathing room. Targets moviepy 2.x and Python 3.x on macOS, Linux, and Windows.
video-storytelling-core-principles
by inclusionAICore storytelling rules for AI video scripts: concrete metaphors instead of abstract jargon, the mute test (story reads without audio), visual contrast and closure, physically visible causes of failure or success, visualizing the “eureka” beat, camera motion tied to physics, in-scene transitions instead of black cuts, character consistency and multi-speaker action/lip sync timelines, and three-act pacing with Mandarin VO speed (~4.5 chars/s) and breathing room for action and SFX.
x-scraper
by inclusionAIX (Twitter) 抓取 skill - 通过 agent-browser (CDP) 抓取指定用户推文或首页推荐流，支持关键词过滤、Tab 切换、多格式输出。使用场景：按用户/关键词抓取时间线、查看首页推荐流、生成 RSS/JSON/Markdown。
ai-video-script-sop-remotion-diffusion
by inclusionAIStandard operating procedure for automated AI video production using a Remotion (code) and diffusion (model) hybrid pipeline. Covers narrative DNA (hero, show-don’t-tell, three-act arc), technical specs (duration, integer segment lengths, resolution, fps, Mandarin pacing), tech-selection matrix (diffusion vs code), a five-part diffusion prompt protocol (style, micro-timing, entities, camera, transitions), end-to-end execution workflow, and a fixed output template (metadata table + per-shot table). Complements create-video and Remotion best-practice skills for execution quality.
ad-image-create
by inclusionAICreate ad-ready product images (single or collage) by back-solving sub-image sizes from target output ratio, grounding scene design with media_comprehension, generating images via image_generator with strict request params and actor-count control, and pairing each deliverable with a short social tagline for 小红书/抖音.
app-evaluator
by inclusionAIA professional skill for App Evaluation (evaluating app's performance with score) and App Improvement (giving professional suggestions for improving the app's performance).
ad-video-create
by inclusionAICreate ad-ready product video from product images, with or without character/subject images. The workflow leverages AI-powered image composition, scene understanding, and video generation. Video prompts should follow commercial shot language—visual hooks, product presence, hero shots, detail showcase, function expression, and dynamic visuals.
media-comprehension
by inclusionAI"An intelligent assistant specialized in handling media files (images/audio/video). **Only for media file analysis**, does not handle document types.\n\n✅ Media files that can be processed:\n- Images: .jpg, .jpeg, .png, .gif, .bmp, .webp, .svg\n- Audio: .mp3, .wav, .m4a, .flac, .aac, .ogg\n- Video: .mp4, .avi, .mov, .mkv, .webm, .flv\n\n❌ Files that cannot be processed (please do not trigger this skill):\n- Documents: .pdf, .doc, .docx, .txt, .md, .rtf\n- Spreadsheets: .xlsx, .xls, .csv, .tsv\n- Presentations: .pptx, .ppt, .key\n- Code: .py, .js, .ts, .java, .cpp, .go, .rs\n- Archives: .zip, .tar, .gz, .rar, .7z\n- Executables: .exe, .bin, .app, .dmg\n- Databases: .db, .sqlite, .sql\n- Configuration files: .json, .xml, .yaml, .yml, .toml, .ini\n- Web pages: .html, .htm, .css\n\n**Trigger conditions**: When the user explicitly requests to analyze image/audio/video content, or when the file extension belongs to the aforementioned media types.". "
tikhub-xiaohongshu-search
by inclusionAILightweight TikHub Xiaohongshu image-search workflow. Prioritizes single-request usage with curl or minimal Python, saves raw API JSON by default, and includes a small stdlib post-processor for CSV and simplified JSON. Use when the user wants Xiaohongshu keyword image search, page-based pagination, or structured note/image metadata from TikHub without a heavy wrapper.
tiktok-download
by inclusionAISingle-file TikTok/Douyin video download and traffic metrics via TikHub API using only httpx; optional persisted raw API JSON plus a stdlib post-processor emitting CSV and simplified JSON. Supports one URL or concurrent batch (max 10 workers). No dependency on any project codebase.
tikhub-youtube-search
by inclusionAILightweight TikHub YouTube search and video-detail workflow. Prioritizes single-request usage with curl or minimal Python, saves raw API JSON by default, and includes a small stdlib post-processor for CSV and simplified JSON. Use when the user wants YouTube comprehensive search results, continuation-token pagination, or structured video metadata from TikHub without a heavy wrapper.
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.