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...
vrm-springbone-physics
by Project-N-E-K-ODebugging and fixing VRM SpringBone physics issues in three-vrm, including hair/clothing physics that flies upward, sticks out horizontally, or behaves unnaturally.
vanilla-js-ui-race-conditions-vrm-vs-live2d
by Project-N-E-K-ODealing with delayed DOM generation, lazy loading, and optimistic state synchronization in vanilla JavaScript without a reactive framework.
vrm-mtoon-outline
by Project-N-E-K-OAdjusting VRM MToon material outline thickness in three-vrm. Covers outline width modes, property access, and how to fix outlines that appear too thick when models are scaled.
ui
by Project-N-E-K-OProject N.E.K.O. 胶囊化 UI、品牌蓝视觉系统规范
i18n
by Project-N-E-K-Oi18n (internationalization) toolkit for projects using i18next. Provides three main functions: (1) i18n-check - Detect hardcoded Chinese text in HTML/JS files, (2) i18n-fix - Replace hardcoded text with i18n markers, (3) i18n-sync - Align translation keys across multiple languages (zh-CN, zh-TW, en, ja, ko, ru, es, pt). Use when working on internationalization tasks, detecting untranslated strings, or syncing locale files.
3d-camera-interaction
by Project-N-E-K-OThree.js 中处理 3D 模型拖拽、缩放、边界检测的正确方法。解决鼠标移动与模型移动不同步、缩放后只能看到模型一部分等问题。
ammo-mmd-physics-hacks
by Project-N-E-K-O应对 WebGL 端 MMD 物理 (Ammo.js) 炸模、穿模、卡顿等极限边缘工况的终极调优指南与 Hack 技巧。
tts-error-reporting
by Project-N-E-K-OConvention for reporting errors from multiprocessing TTS workers to the main process frontend. Use this skill when modifying, adding, or debugging TTS workers in tts_client.py to ensure connection errors, quotas, and API limits correctly display Toast notifications to the user rather than failing silently.
ssr-hydration-scraping
by Project-N-E-K-OBest practices for extracting data from modern React/Vue SSR pages (like Next.js or Nuxt.js) by targeting hydration state blocks (__NEXT_DATA__, __NUXT__) using regex and `jmespath`, avoiding brittle DOM selector scraping.
push-to-pr
by Project-N-E-K-OPush commits to an existing GitHub PR's source branch. NEVER create new branches. Use when the user says "push to PR
gemini-openai-api
by Project-N-E-K-OGemini 模型通过 OpenAI 兼容 API 接入指南。包含：(1) 辅助 API 配置（summary/correction/emotion/vision），(2) extra_body 格式用于控制 thinking，(3) 响应格式处理（markdown 代码块）。当需要将 Gemini 作为辅助模型接入、配置 thinking 参数、或处理 Gemini API 返回格式时使用。
pytest-asyncio-httpx-mocking
by Project-N-E-K-OWhen masking httpx.AsyncClient with unittest.mock in Pytest, AsyncMock must be used instead of MagicMock for async methods like post/get to prevent TypeError when awaited.
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.