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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xingyin-data-analyst
by xfs96192兴银理财多资产投资部数据统计分析助手。用于分析部门产品的规模、业绩、持仓、投资经理归因等数据。当用户询问"统计部门规模"、"产品业绩"、"持仓分析"、"投资经理产品数据"、"渠道分布"、"招商产品"、"外币产品"等相关问题时触发。支持从产品运作概览、持仓盈亏、净值数据、业绩指标等多个数据源进行查询和计算。
khazix-writer
by xfs96192数字生命卡兹克（Khazix）的公众号长文写作skill。当用户需要撰写公众号文章、写稿子、续写文章、根据素材产出长文时使用。触发词包括但不限于：写文章、写稿子、帮我写、续写、扩写、公众号文章、长文、出稿、按我的风格写。即使用户只是说"帮我把这个写成文章"或"用我的风格写一下"，只要上下文涉及内容创作和公众号输出，都应该触发。也适用于用户丢过来一个PDF、brief、新闻链接、语音转文字或任何素材说"帮我写篇文章"的场景。不要用于短内容（小红书帖子、推特、朋友圈）或纯标题摘要生成（那个用wechat-title skill）。
rqdata
by xfs96192RiceQuant (米筐) rqdatac Python金融数据库查询工具。通过rqdatac SDK从米筐RQData服务端获取中国金融市场全品种数据。 覆盖品种：A股(行情/财务/因子/分红/行业)、港股、期货(主力合约/仓单/升贴水)、期权(Greeks/衍生指标)、 指数与ETF(成分/权重)、公募基金(净值/持仓/经理)、可转债(转股/强赎/衍生指标)、风险因子(暴露度/协方差)、 现货、货币市场(SHIBOR/国债回购)、宏观经济、另类数据(一致预期/ESG)。 当用户要求"通过ricequant/米筐/rqdata获取数据"、"查询A股行情"、"获取期货主力合约"、"查基金净值"、 "获取收益率曲线"、"查转债数据"、"获取SHIBOR"、"rqdatac查询"等金融数据查询任务时触发。 支持日线/分钟线/tick/周线多频率，支持实时行情推送。 **重要：直接运行查询并返回数据结果，不仅仅生成代码。自动校验和修正代码格式。**
xfs-market-data
by xfs96192查询兴银理财市场数据库（xfs-market），通过 REST API 调用 https://market.xiafansheng.com 获取金融时间序列数据。覆盖9大类400+指标：A股指数(000300.SH/000016.SH/中证指数等)、中债国债/国开债到期收益率曲线(E1xxx)、宏观经济数据(CPI/PMI/GDP/M2/社融，EMM/EMI/EMG开头)、SHIBOR/DR007/IRS资金利率、汇率、商品期货、可转债指数、海外指数。当用户问到查询市场数据、A股走势、债券收益率、宏观经济数据、CPI/PMI/GDP、利率数据、汇率商品价格、市场指标、xfs-market、市场数据库时务必使用此skill。支持搜索指标、获取时间序列、新增删除指标、触发数据更新。
xfs-server
by xfs96192连接并管理夏凡盛的阿里云服务器（47.115.210.223）。当用户说"帮我管理服务器"、"服务器xxx"、"重启服务"、"查看日志"、"部署项目"、"修改Nginx"、"查看状态"等涉及服务器操作的请求时使用此技能。
didi-ride-skill
by xfs96192中国城市出行服务。当用户表达任何交通出行需求时必须使用此技能——包括打车/叫车/网约车、查价格、路线规划（公交/驾车/步行/骑行）、周边搜索、查询订单/司机位置/取消订单。关键词："打车"、"叫车"、"去[地点]"、"回家"、"上班"、"下班"、"查价格"、"多少钱"、"路线"、"怎么走"、"步行到"、"附近"、"周边"、"司机"、"订单"、"查询订单"。注意：即使用户未明确说"打车"，只要涉及从A地到B地、通勤、或交通方式选择，都应触发。不触发场景：开发打车应用、使用其他导航app、订外卖、查公交时刻表、股票/财报查询。
tech-earnings-deepdive
by xfs96192科技股财报深度分析与多视角投资备忘录系统（v3.0）。覆盖A-P共16大分析模块、6大投资哲学视角、机构级证据标准、反偏见框架和可执行决策体系。当用户提到某科技公司财报分析、季报/年报解读、earnings call、收入增长分析、利润率变化、guidance指引、估值模型、DCF、反向DCF、EV/EBITDA、PEG、Rule of 40、管理层分析、竞争格局、持仓判断、是否买入/卖出/加仓某科技股、某公司最新财报怎么看、帮我做个deep dive、多角度估值、投资大师怎么看这家公司、variant view、key forces、kill conditions、筹码分布、高管团队、合作伙伴生态、宏观政策影响等话题时，务必使用此技能。即使用户只是笼统地问"帮我看看NVDA最新财报"或"META这季度表现如何"或"该不该继续持有MSFT"，也应触发此技能来提供全面的财报分析和多视角投资备忘录。此技能与us-value-investing技能互补——us-value-investing侧重长期价值四维评分，本技能侧重最新财报的深度拆解、多投资哲学的综合判断、以及可执行的持仓决策。
cicc-research-analyst-yzh-skill
by xfs96192复刻于钟海分析师专属研究框架、研判逻辑与个人投研风格，完整还原其独有分析视角与研究习惯，适配软件AI相关行业及个股深度拆解、赛道逻辑解读、板块行情研判等场景。用户询问“老于，帮我分析XXXX”，“老于，怎么看XXXXX”以 “老于”昵称开头的问题，使用此技能，用户问软件、AI行业的行业及个股问题时也调用此技能。
neodata-financial-search
by xfs96192NeoData Financial Search — 自然语言通用金融数据搜索服务。用自然语言查询股票、基金、指数、板块、 宏观经济、外汇、大宗商品等全品类金融数据，涵盖行情报价、财务报表（财报）、资金流向、研报评级、 事件公告等，支持结构化API数据和财经文章两种召回模式，即问即答。 Use when the user asks about financial data, stock quotes, financial statements, earnings reports, market data, fund info, macro economics, forex, commodities, or needs to query the NeoData API.
wechatpay-basic-payment
by xfs96192微信支付基础支付解决方案，涵盖支付、退款账单、分账、商户进件、开户意愿确认，提供选型/代码示例/业务速查/质量评估/排障五大能力。Use when user mentions "JSAPI支付", "APP支付", "H5支付", "Native支付", "小程序支付", "付款码支付", "合单支付", "特约商户进件", "开户意愿确认", or asks to "推荐支付方式", "要支付接口代码示例", "排查支付或退款问题".
tencentcloud-ocr-recognizetableaccurate
by xfs96192腾讯云表格识别v3(RecognizeTableAccurateOCR)接口调用技能。当用户需要从表格图片或PDF中识别常规表格、无线表格、多表格的内容,提取每个单元格的文字信息,或将表格图片识别结果导出为Excel文件时,应使用此技能。支持中英文表格图片、旋转表格图片、嵌套表格图片等复杂场景,识别效果优于表格识别V2。
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.