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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market-sentiment-timing
by FeiCoder市场情绪择时策略，基于投资者情绪指标和主成分分析构建情绪指数。适用于需要利用市场情绪进行大盘择时的场景。
strategy-selection
by FeiCoder量化投资策略组合选择指南，根据市场环境（牛/熊/震荡）选择合适的选股和择时策略组合。适用于需要构建或调整量化投资组合、不知道如何搭配使用多种策略的投资者。
capital-flow
by FeiCoder资金流选股模型指南，包括资金流概念、测算方法和基于高频数据的选股策略。适用于需要利用资金流向进行选股决策的场景。
momentum-reversal
by FeiCoder动量与反转策略指南，包括行为金融学基础、动量反转效应的成因和A股量化策略。适用于需要理解和实施动量/反转投资策略的场景。
multi-factor-model
by FeiCoder多因子选股模型的完整指南，包括因子选取、有效性检验（排序法）、冗余因子剔除、综合评分模型构建和实证案例。适用于需要构建或优化多因子选股策略的量化投资者。
quant-stock-selection-intro
by FeiCoder量化选股基础知识，介绍量化投资的概念、框架以及与传统投资方法的区别。当需要理解量化选股的核心理念、适用场景或构建量化选股系统时使用此 skill。
sector-rotation
by FeiCoder行业轮动策略指南，基于宏观经济周期和M2指标的A股行业轮动模型。适用于需要理解和实施行业配置策略的场景。
style-rotation
by FeiCoder风格轮动策略指南，包括价值/成长风格、大小盘风格的轮动规律和量化模型。适用于需要理解和实施A股市场风格轮动投资的场景。
bull-bear-line
by FeiCoder牛熊线择时模型，基于几何布朗运动理论判断大盘牛熊转换。适用于需要识别市场重大转折点的场景。
hurst-timing
by FeiCoderHurst指数择时模型，基于分形市场理论和R/S分析方法判断市场趋势拐点。适用于需要识别市场长期记忆性和趋势转折的场景。
statistical-arbitrage
by FeiCoder统计套利策略的完整指南，包括配对交易、协整分析、主成分策略和行业轮动套利。适用于需要构建市场中性策略、对冲系统性风险并获取稳定Alpha收益的量化投资场景。
quant-timing-intro
by FeiCoder量化择时基础知识，介绍量化择时的概念、框架和主要方法论。适用于需要理解择时策略基本框架的场景。
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.