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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remotion-vinyl-player
by vibe-motionCreates an elegant, realistic Vinyl Record Player animation component for Remotion. Use when needing a music player UI, album showcase, or audio-visualizer interface in a video. (Keywords: 黑胶唱片, 音乐播放器, 唱片机, 专辑展示, 音频可视化)
wechat-2d-render
by vibe-motionClone or update https://github.com/sxhzju/wechat-2d and render the default WeChat-style 2D chat motion video with Remotion. Use when users ask for 微信聊天动画, wechat 2d chat render, 微信视频消息动效, or exporting the default demo from the wechat-2d project.
threejs-earth-render
by vibe-motionClone or update https://github.com/vibe-motion/threejs-earth and render the Three.js Earth route animation with Puppeteer frame capture. Use when users ask for 三维地球航线动画, Three.js Earth, 地球飞线, globe route animation, or exporting an Earth GIF/MP4/PNG sequence.
svg-assembly-animator
by vibe-motion为 SVG 矢量图创建充满“力量感”与“速度感”的零件组装动画，并支持一键导出 30fps 的透明背景序列帧。适用于需要将静态 SVG 转换为可用于视频剪辑（如 AE/PR）的透明素材场景。
ruler-progress-render
by vibe-motionClone or update https://github.com/sxhzju/ruler-progress-animator and render a ruler progress video with default parameters. Use when users ask for requests like "绘制个尺子进度条", "做个尺子进度动画", "渲染 ruler progress", or ask to export the default demo video from this project.
remotion-3d-ticker
by vibe-motionCreates infinite 3D vertical scrolling ticker animations in Remotion. Use when you need to build a parallax gallery, infinite image scroll, multi-column continuous vertical scrolling effect, or a 3D photo wall (3D照片滚动墙 / 3D相册瀑布流).
procedural-fish-render
by vibe-motionClone or update https://github.com/vibe-motion/procedural-fish and render procedural-fish animation to a video using the project's own render command. Use when the user asks to render 程序鱼/procedural fish, export a 程序鱼视频, or run procedural-fish Remotion rendering.
light-spotlight-render
by vibe-motionGenerate a swinging spotlight text-reveal HTML animation with configurable text, swing angle, lamp scale, glow, and colors. Use when users ask for 聚光灯扫字动画, spotlight text reveal, light logo reveal, 发光文字揭示动画, or want a reusable HTML animation instead of a static image.
claude-typer
by vibe-motionRender a Claude-style prompt typing animation video by calling Remotion CLI against the remote site https://www.laosunwendao.com. Use when the user asks for "做一个 claude 的提示词打字机动画", "做 Claude 打字动画", "创建提示词动画", or similar requests that convert a text prompt into a typing-animation video.
pixel2motion
by vibe-motionTurn a raster logo (PNG/JPG/WebP/screenshot) into a clean minimal SVG with edge smoothness as the primary hard gate and IoU optimized as high as reasonably possible without a fixed global threshold, then into a choreographed logo animation delivered as standalone JS-rendered HTML, applying Disney's 12 animation principles. Use when asked to animate a logo, build a logo reveal / splash screen / brand intro, convert a logo image into animated SVG or HTML, add motion to a vectorized mark, or create loading/idle/hover motion for a brand mark. v2: also handles self-crossing draw-on choreography (split-fill, exact easing subdivision, tip glint), closed variable-width ribbon fitting, and quantitative motion QA (easing probe, ink-delta continuity sweep).
disney-animation-rule-skill
by vibe-motionApply Disney's 12 principles as practical design and engineering rules for procedural animation. Use when creating, improving, reviewing, or debugging code-driven motion in web, SVG, canvas, React, Remotion, game, UI, character, camera, or 3D scenes; especially when motion feels stiff, weightless, mechanical, unclear, or physically correct but visually weak.
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.