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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m3u8-media-downloader
by lzwmeUse @lzwme/m3u8-dl for media download and video info parsing. Use when the user mentions video/music download (m3u8/HLS/mp4/mp3 or 抖音/皮皮虾/微博视频), or 获取视频信息、解析视频链接, and a video/music URL is present.
akshare
by lzwmeAKShare 开源金融数据接口库，提供股票、期货、期权、基金、外汇、债券、指数、加密货币等全品类金融数据；当用户需要获取各类金融市场数据时使用
backtrader
by lzwmeBacktrader 开源量化回测框架，支持多数据源、多策略、多周期回测与实盘交易，纯Python实现。当用户需要开发量化策略、进行回测分析、编写交易逻辑、回测参数优化，或提及 backtrader、量化回测框架时使用。若用户仅需数据获取而无回测需求，引导使用 baostock/akshare/tushare 等数据 Skill。
akquant
by lzwme生成 akquant 框架的可执行量化策略代码，涵盖数据接口、事件驱动、风控与优化。当用户需要开发量化策略、配置回测环境、设置风控规则、进行参数优化、实现横截面轮动策略，或提及 akquant 时使用
tushare
by lzwmeTushare 专业金融数据平台，提供A股行情、历史K线、财务数据、宏观数据、指数数据等，覆盖全面、数据质量高。当用户提及 Tushare、获取A股数据、查询财务报表、下载宏观数据，或需要高质量、多维度金融数据时使用。与 baostock（免费但功能有限）和 jqdatasdk（需注册但有积分门槛）不同，tushare 数据最全但需注册获取 Token。免费替代方案可用 baostock 或 akshare。
tdxquant
by lzwme通达信量化数据获取技能。当用户提及 tdxquant、通达信、TdxQuant、tqcenter，并且需要获取A股数据（行情快照、K线、财务、板块、可转债、新股、交易数据等）、查询交易日历、执行通达信公式、订阅行情、交易下单时使用
rqalpha
by lzwmeRQAlpha 米筐开源事件驱动回测框架。支持A股和期货，模块化架构，可自由扩展；当用户需要快速回测A股/期货策略、使用内置数据（download-bundle）、开发Mod插件，或提及 rqalpha、米筐时使用。与 backtrader 相比，rqalpha 内置A股日线数据、安装更简便，适合快速验证；backtrader 更灵活、社区更大。若用户仅需数据获取而无回测需求，引导使用 baostock/akshare/tushare 等数据 Skill。
qmt-docs
by lzwmeQMT（迅投极速策略交易系统）Python 策略开发完整指南。涵盖策略编写、回测、实盘交易、API参考和代码示例。当用户需要开发 QMT 量化策略、查询 QMT API、从聚宽迁移至 QMT、编写实盘交易程序，或提及 QMT、迅投策略、QMT 回测时使用。
pywencai
by lzwme同花顺问财数据查询：使用中文自然语言查询A股、指数、基金、港美股、可转债等市场数据；当用户需要通过自然语言从问财获取选股、财务、资金流、技术指标等数据时使用。
miniqmt
by lzwmeMiniQMT 迅投量化交易接口，基于 XtQuant Python 库，支持 A 股/期货/期权的行情数据获取（K线、分笔、财务数据等）和交易下单（报单、撤单、查询资产/委托/持仓）。当用户提及 miniqmt、xtquant、迅投、获取实时行情、量化交易下单、回测数据获取，或需要连接 MiniQMT 客户端进行程序化交易时使用
jqdatasdk
by lzwme聚宽（JoinQuant）本地数据 SDK，提供 A 股行情、历史K线、财务数据、指标数据等，与聚宽官网数据同源。当用户提及 jqdatasdk、聚宽数据、需要获取A股数据，或已注册聚宽账号时使用。与 baostock（免费）和 tushare（数据最全）不同，jqdatasdk 需注册聚宽账号获取 Token，适合已在使用聚宽平台的用户。本地数据获取请用本技能，聚宽官网策略开发请用 joinquant-docs 技能。
joinquant-docs
by lzwme聚宽（JoinQuant）官网策略开发指南，涵盖回测、模拟交易、数据 API、交易函数、因子与技术指标。当用户编写聚宽策略、回测、模拟盘、查询聚宽 API、get_price/order/run_daily、Alpha 因子、技术指标，或提及 joinquant、聚宽、jqdata 策略时使用。本地数据获取请用 jqdatasdk 技能。
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.