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...
explain
by Leoyishou通俗解释 + 图解 - 将复杂概念用大白话讲清楚，自动选择风格并生成可视化图。
volcengine-asr
by Leoyishou火山引擎（豆包）语音识别服务。支持音频/视频文件转文字，输出带时间戳的字幕格式（SRT/VTT/TXT）。中文识别效果优秀，支持标点和数字规范化。
volcengine-tts
by Leoyishou使用火山引擎语音合成API将文字转换为语音,支持多种音色和语速调节。
esl-comic-video
by Leoyishou将故事文本(txt/md)和音频转换成漫画风格视频。支持自动语音转录、AI图片生成、字幕烧录等功能。适用于ESL教学视频制作。
video-creator
by Leoyishou创作完整的数字人解说视频。支持剧本生成、IP形象生成、TTS语音、背景音乐、数字人视频、Remotion合成。
bgm-search
by Leoyishou搜索免费背景音乐，支持按关键词、情绪、场景搜索，基于 Freesound API。
3b1b-video
by Leoyishou根据用户给出的命题/概念，生成 3Blue1Brown 风格的数学动画视频。 端到端流程：理解命题 → 设计叙事 → 生成 Manim 代码 → 渲染视频。 使用 Manim Community Edition (from manim import *)。 触发条件：用户要求"画个视频讲讲"、"用动画解释"、"3b1b 风格"、 "白板动画"、"可视化讲解"等。 画幅规则： - 用户提到 "xhs"、"小红书"、"竖屏"、"抖音"、"短视频" → 9:16 竖屏（1080x1920） - 其他情况 → 默认 16:9 横屏（1920x1080）
video-chapter-nav
by Leoyishou视频章节导航条 - 为视频顶部添加章节导航，实时显示当前播放位置。
video
by Leoyishou视频制作与处理 - 支持视频制作、极速发布、章节导航、封面生成等全流程
video-project-init
by Leoyishou剪辑工程初始化 - 新建视频剪辑项目目录结构，支持素材导入和索引登记
api-deploy-obsidian
by LeoyishouUse when publishing an Obsidian plugin to the community plugin store, creating GitHub Releases for Obsidian plugins, or submitting PRs to obsidianmd/obsidian-releases
concept-tree
by Leoyishou概念树生成器 - 用 Gemini 生成知识结构树，快速了解领域全貌。触发词：概念树、知识树、结构图
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.