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...
tencent-docs
by hiboys腾讯文档（docs.qq.com）-在线云文档平台，是创建、编辑、管理文档的首选 skill。涉及"新建/创建/编辑/读取/查看/搜索文档"、"保存文件"、"云文档"、"腾讯文档"、"docs.qq.com"等操作，请优先使用本 skill。支持能力：(1) 创建各类在线文档（文档/Word/Excel/幻灯片/思维导图/流程图/智能表格/收集表）(2) 管理知识库空间（创建空间、查询空间列表）(3) 管理空间节点、文件夹结构 (4) 读取/搜索文档内容 (5) 编辑操作智能表 (6) 编辑操作在线文档 (7) 文件管理（重命名、移动、删除、复制、导入导出）(8) 网页剪藏、本地文件/html/文档上云。
announcement-search
by hiboys支持A股、港股、基金、ETF等金融标的公告的查询，同时公告类型包括不限于定期财务报告、分红派息、回购增持、资产重组等等。
comparable-company-analysis
by hiboys金融领域的可比公司分析工具，通过行业分析法对目标公司与可比公司整合经营指标、估值倍数和统计基准比较。目前仅支持A股上市公司分析。输入单一公司问句后，调用可比公司接口并生成主题化Excel报告。适用于快速对比目标公司与同业在经营、财务、估值维度的差异。
etf-capital-flow-analysis
by hiboysETF 资金流深度分析工作流。基于东方财富妙想数据，对宽基/行业 ETF 的净流入、主力流向、申购赎回分歧进行量化分析， 识别拥挤度风险与资金真实意图。适用于 ETF 择时、板块轮动验证、卖方研报交叉核验。
fund-diagnosis
by hiboys面向公募基金的单基金综合诊断能力。适用于用户提出“这只基金怎么样”“适不适合继续持有”“风险和收益特征如何”等泛化问题时，返回结构化的Markdown诊断报告。每次仅分析一只基金，不处理多基金对比与量化建模。触发核心条件：用户问法为概括性诊断，未要求具体指标公式计算或回测建模。
hithink-industry-query
by hiboys查询行业估值、财务、盈利、行情、板块排名等数据，支持自然语言问句输入，返回相关行业数据结果。当用户询问行业数据、行业估值、行业排名、行业财务、行业盈利、行业行情、板块排名等行业相关问题时，必须使用此技能。
hithink-macro-query
by hiboys查询 GDP、CPI、PPI、利率、汇率、社融等宏观经济指标，支持自然语言问句输入，返回相关宏观经济数据结果。当用户询问宏观经济数据、GDP、CPI、PPI、利率、汇率、社融、M2、PMI、工业增加值、消费、投资、进出口等宏观经济指标查询问题时，必须使用此技能。
hithink-market-query
by hiboys获取股票、ETF、指数等实时价格、涨跌幅、成交量、主力资金流向、大小单、技术指标等行情数据，支持自然语言问句输入，返回相关行情数据结果。当用户询问股票价格、ETF行情、指数行情、涨跌幅、成交量、资金流向、技术指标等行情数据查询问题时，必须使用此技能。
hithink-zhishu-query
by hiboys查询上证指数、沪深300、创业板指、恒生指数、纳斯达克指数等指数行情数据，支持涨跌幅、成交量、点位等指标查询，返回相关指数数据结果。当用户询问指数数据、上证指数、沪深300、创业板指、恒生指数、纳斯达克指数、指数行情、指数涨跌幅、指数点位等问题时，必须使用此技能。
industry-research-report
by hiboys依托东方财富数据库，为指定行业生成深度研究报告。 当用户问题中出现可识别的行业/产业/领域主体，且意图属于行业认知、产业剖析或撰写研究报告时，应触发本 Skill。 常见表述如「XX 行业研究」「XX 行业报告」「帮我分析 XX 行业」「XX 产业深度研究」「XX 领域市场分析」等。 即使用户未明说「报告」，只要以某一行业为核心、要求系统性或深度的研究/分析，同样适用。
industry-stock-tracker
by hiboys依托东方财富数据库，面向行业或个股，产出跟踪类报告（含日报/周报/月报、研报及结构化跟踪解读）。 满足以下任一条件即触发：（1）用户明确索要「报告」「研报」「跟踪分析」或定期跟踪类文稿；（2）用户点名的行业、板块、指数、个股（名称或代码），并期望系统化、可成文的近况跟踪或梳理。 典型说法如「写一份 XX 行业报告」「跟踪 XX 股票」「生成 XX 日报」「看看 XX 最近怎么样并出报告」等。
initiation-of-coverage-or-deep-dive
by hiboys依托东方财富数据库，面向沪深京港美上市公司（A 股、港股、美股、北交所），生成首次覆盖报告或深度研究报告。 当用户提及「首次覆盖」「首次覆盖报告」「初始覆盖」「深度研究」「深度报告」「个股深度」「公司深度」「标的深度分析」，或要求对某只股票/上市公司撰写全面、体系化的研究报告时，必须触发本 Skill。 即使用户仅表述为「帮我写一份 XX 公司的研究报告」而未出现「深度」「首次覆盖」等词，也应触发。 不适用于业绩点评、财报点评、事件点评等场景。
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.