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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rq-report-renderer
by ricequant将 Markdown 研究报告渲染为专业 HTML 文档。输入是其他 skill 已生成的 Markdown 报告，输出是可浏览、可打印的单文件 HTML。 务必使用此技能当用户： - 明确要求把 Markdown 报告转换成 HTML - 想生成网页版研报或打印版页面 - 已经有 `.md` 报告，只差最后的渲染步骤 不适用场景： - 还没有 Markdown 报告 - 需要 PDF / Word 导出
rq-sector-overview
by ricequant创建模板驱动的行业概览报告，基于显式行业股票池与真实财务/估值/价格数据完成行业层面的结构化分析。 使用 RQData CLI 获取股票池、行业分类、财务、估值与区间价格，再由 sector-overview/scripts/generate_report.py 严格按照 `assets/template.md` 生成 Markdown，并在本地可用时渲染 HTML。 务必使用此技能当用户： - 明确请求行业报告、行业概览、sector overview、行业研究 - 想看某个赛道/板块的整体财务、竞争格局和估值位置 - 需要行业内公司对比、龙头排序、集中度分析 - 需要从行业层面筛选潜在投资机会 不适用场景： - 单一公司首次覆盖 -> initiating-coverage - 财报后的单公司点评 -> earnings-analysis - 只要一句话介绍行业
rq-thesis-tracker
by ricequant创建模板驱动的投资论文跟踪报告，系统化跟踪核心观点、关键支柱、资本回报、催化剂和风险信号。 `RQData CLI` 负责财务、价格、估值、公告、分红和股东结构等结构化主数据；若需要补充公司新闻、管理层变化、行业趋势、竞争格局或分析师观点，可额外使用 `web_search` 获取实时信息，并先落为结构化 JSON，再由 thesis-tracker/scripts/generate_report.py 以客户可读方式纳入最终正文。 务必使用此技能当用户： - 明确请求投资论文、thesis、投资逻辑、论文跟踪 - 想建立或更新投资框架、验证关键假设、更新观点 - 需要追踪催化剂、里程碑、资本回报或信念度变化 - 需要把既有 thesis 和最新数据做系统化对照 不适用场景： - 首次覆盖深度研究 -> initiating-coverage - 财报发布后的单次点评 -> earnings-analysis - 只需简单投资建议且不需要完整跟踪报告
rq-initiating-coverage
by ricequant创建模板驱动的首次覆盖研究报告，基于真实财务、股权结构、交易、分红、市场预期、卖方摘要与可比公司数据输出长篇结构化报告。 `RQData CLI` 负责财务、交易、分红、预期、可比公司与公司基础资料主数据；若需要补充管理层履历、行业规模、竞争格局或政策背景，可额外使用 `web_search` 获取实时定性信息，并先落为结构化 JSON，再由当前报告脚本将其以客户可读方式纳入对应章节。 务必使用此技能当用户： - 明确请求首次覆盖、initiating coverage、深度公司研究、完整公司分析框架 - 需要为新纳入跟踪的公司建立完整的公司研究报告、对比框架和估值定位 - 需要公司概况、财务轨迹、股权结构、卖方预期、可比估值等一揽子分析输入 不适用场景： - 财报后单次复盘 -> earnings-analysis - 财报前瞻 -> earnings-preview - 行业整体研究 -> sector-overview
rqdata-python
by ricequantRQData数据API使用指南。当需要查询RQData数据接口、获取金融数据时使用。支持A股、港股、期货、期权、指数、基金、可转债等市场数据查询，包含HTTP API和Python API文档。
rq-catalyst-calendar
by ricequant创建模板驱动的催化剂日历报告，追踪覆盖股票池未来 30 天的重要事件和催化剂。 使用 RQData CLI 获取财报、分红、公告等结构化公司事件；当需要补充宏观、行业会议或政策催化时，可额外使用 `web_search` 获取实时信息，并先落为结构化 JSON，再统一生成 Markdown 和 HTML 报告。 务必使用此技能当用户： - 明确请求催化剂日历、事件日历、earnings calendar、upcoming events - 想了解接下来一段时间的重要事件、本周/本月重点日期 - 需要追踪股票池的重要催化剂 - 需要把公司事件与宏观 / 行业催化放进同一份时间轴报告 不适用场景： - 单个公司财报深度分析 -> earnings-preview / earnings-analysis - 行业整体分析 -> sector-overview - 只需回答单一事实且不需要完整日历报告
rq-earnings-analysis
by ricequant创建模板驱动的财报分析报告，在财报披露后基于真实财务数据、市场预期、公告原文链接、卖方研报和股价反应完成结构化复盘。 `RQData CLI` 负责财务、估值、价格、公告和一致预期主数据；若需要补充财报电话会、管理层动态、行业或政策语境，可额外使用 `web_search` 获取实时信息，并先落为结构化 JSON，再统一生成 Markdown 与 HTML 报告。 务必使用此技能当用户： - 明确请求财报分析、季度业绩点评、earnings update、post-earnings report - 想知道财报发布后是超预期、符合预期还是低于预期 - 需要结合财务数据、市场预期和股价反馈做复盘 不适用场景： - 财报前瞻 -> earnings-preview - 首次覆盖 -> initiating-coverage - 只要一句话快评
rq-earnings-preview
by ricequant创建模板驱动的财报预览报告，在财报发布前基于历史财务、近期股价、卖方一致预期、研报口径与网络搜索结果搭建可追踪的预判框架。 `RQData CLI` 负责历史财务、价格、一致预期、研报与公告主数据；若需要补充目标季度预计披露日、电话会安排或近期行业动态，可额外使用 `web_search` 获取实时信息，并先落为结构化 JSON，再由 earnings-preview/scripts/generate_report.py 以客户可读方式纳入正文。 务必使用此技能当用户： - 明确请求财报预览、earnings preview、pre-earnings、财报前瞻 - 想知道财报前看什么、哪些指标最关键 - 需要结合市场预期和近期股价定位财报前 setup - 需要在财报前形成一份结构化关注清单和情景分析 不适用场景： - 财报发布后的复盘分析 -> earnings-analysis - 首次覆盖深度研究 -> initiating-coverage - 只问一句“什么时候发财报”
rq-idea-generation
by ricequant创建模板驱动的投资创意生成报告，基于真实股票池与财务指标完成系统化量化筛选，并在必要时结合主题/政策/行业实时信息做第二阶段验证，输出价值、成长、质量三类候选及其跟踪重点。 `RQData CLI` 负责股票池、公司元数据、财务与估值因子主数据；`web_search` 只在需要补充主题验证、政策催化或行业动态时使用，且必须先落为结构化 JSON，再由当前 LLM 基于结构化快照和 `assets/template.md` 回写客户可读正文，最后渲染 HTML。 务必使用此技能当用户： - 明确请求投资创意、idea generation、找投资机会、系统化选股 - 需要对一个股票池做价值 / 成长 / 质量筛选 - 想快速形成一份带量化依据的候选清单 - 需要把筛选规则、候选结果和跟踪重点整理成正式报告 不适用场景： - 单个公司首次深度覆盖 -> initiating-coverage - 财报发布后的单家公司分析 -> earnings-analysis - 只要一句话推荐且不需要完整报告
rq-morning-note
by ricequant创建模板驱动的晨会纪要报告，汇总隔夜公告、最近披露的财务更新、昨日股价表现、盘前宏观/行业语境与今日重点观察名单。 `RQData CLI` 负责个股行情、公告、财报、分红等结构化主数据；若需要补充宏观政策、海外市场、行业新闻或监管变化，可额外使用 `web_search` 获取实时信息，并先落为结构化 JSON，再由当前脚本纳入最终正文。 务必使用此技能当用户： - 明确请求晨会纪要、morning note、晨会准备、morning meeting - 想了解隔夜动态、昨晚发生了什么、overnight developments - 询问今天看什么、今日重点、what to watch today - 需要盘前关注名单或交易观察 不适用场景： - 单一公司财报深度点评 -> earnings-analysis / earnings-preview - 首次覆盖深度研究 -> initiating-coverage - 仅需简单新闻摘要且不需要完整报告
rqams
by ricequant处理 RQAMS 数据查询和操作时使用。标准产品、workspace、交易流水、估值表、持仓报表、模拟交易、对账、报表和分析任务优先路由到 rqams-cli；需要 Python SDK 脚本、多 API 组合处理、本地 Python 环境诊断或 CLI 未覆盖接口时路由到 rqamsc-python。
rqams-cli
by ricequant使用本地 rqamsc CLI 查询和操作 RQAMS 时使用，覆盖产品、workspace、交易流水、估值表、持仓报表、模拟交易、对账、报表和分析数据等标准任务。
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.