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...
wechat-converter
by z0gSh1u将通用写作 Skill 产出的内容转换为适合微信公众号平台发布的格式和风格。 公众号偏好正式但不生硬、排版讲究、有深度的内容风格。
content-creator
by z0gSh1u多平台内容创作主入口。协调整个创作流程：研究→配图→写作→润色→平台转换。 用于创作文章、写内容、做选题研究并发布到知乎/小红书/公众号。
deep-research
by z0gSh1u对给定选题进行联网深度研究，收集整理资料用于后续内容创作。 自动检测可用的网络搜索工具（WebSearch 或 MCP 搜索工具），无可用工具时回退到 DDGS。 输出结构化的 Markdown 资料汇总，包含来源引用。
general-writing
by z0gSh1u整合深度研究、图片搜索、图片处理等原子 Skill 的成果，创作图文并茂的通用文章。 产出的文章是一种"中间体"，可进一步转换为不同平台的格式。
humanizer-cn
by z0gSh1u识别并消除中文文本中的 AI 生成痕迹，使文章更自然、更像人类创作。 基于中文语境的 AI 写作特征检测，包括套话、过度修饰、机械结构等。 参考 Wikipedia "Signs of AI writing" 指南，并针对中文进行本地化。
image-processing
by z0gSh1u对图片进行处理，支持在图片中插入配文。 两种配文模式：底部边框式（像画框一样）和内部贴纸式（类似小红书效果）。 使用 PIL/Pillow 实现，支持中文和 Emoji。
image-search
by z0gSh1u对给定选题进行联网图片搜索，查找适合配图的素材。 使用 DuckDuckGo 图片搜索，支持尺寸、颜色、类型、版权过滤。 返回图片 URL、缩略图、来源等结构化信息。
xiaohongshu-converter
by z0gSh1u将通用写作 Skill 产出的内容转换为适合小红书平台发布的格式和风格。 小红书偏好生活化、亲切感、有颜值的内容风格，Emoji 适度使用不过密。
zhihu-converter
by z0gSh1u将通用写作 Skill 产出的内容转换为适合知乎平台发布的格式和风格。 知乎偏好专业深度、逻辑清晰、有理有据的内容风格。
github-stars-searcher
by z0gSh1uSearch through your GitHub Stars collection using semantic understanding. Use when user wants to: (1) Find a previously starred repository, (2) Search for repos by functionality or use case, (3) Browse their starred repositories with natural language queries like "authentication library" or "image processing tool". Requires github-stars-collector to be run first to build the database.
investment-analysis
by z0gSh1u分析中国及全球金融市场，使用每日更新拉取到本地的价格数据和新闻，输出投资分析报告。包含市场概览、技术面分析、消息面解读、跨市场联动、前瞻研判、风险提示等内容。指数包含：上证、沪深300、创业板、恒生、标普500、纳斯达克，贵金属包含：沪金、沪银。
github-stars-collector
by z0gSh1uSync GitHub starred repositories to local directory structure for semantic search. Use when user wants to: (1) Initialize their GitHub Stars collection, (2) Update collection with newly starred repos. Runs as a single Python script — no LLM involvement, fast and reliable.
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.