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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bug-hunter
by digoal根据用户给出的行业名称（或默认使用热点社会新闻），抓取最新新闻热点，用"找世界bug"的视角——找出那些打眼一看就不合理的地方——深度分析根因、利益链条、监管盲区，并给出多维度解决思路，输出图文并茂（含SVG/Mermaid/ASCII图形）的Markdown文件，保存到项目markdown/目录。 触发条件：用户提到"找bug"、"社会bug"、"不合理现象"、"帮我分析这个行业的问题"、"帮我找新闻里的bug"、"找热点新闻的漏洞"、"有什么不合理的地方"、"帮我分析为什么会这样"、"bug猎手"等。即使用户只说"帮我找找最近有什么奇怪的新闻"或"这个行业有哪些不合理"，也应使用本 skill。
book-note-writer
by digoal生成深度读书笔记的专用 Skill。输入书籍的豆瓣链接（或书名+作者），自动搜索书籍信息、高质量书评、学术评价等素材，结合 Claude 的深度思考，生成图文并茂、观点鲜明、适合分享的读书笔记，以 Markdown 格式保存到项目 markdown/ 目录。触发条件：用户提供豆瓣链接、提到"读书笔记"、"书评"、"帮我分析这本书"、"写一篇XX书的笔记"，或任何希望深度理解一本书的场景。即使用户只说"帮我写一下这本书的感悟"，也应使用本 skill。
industry-deep-explainer
by digoal快速且通俗易懂地讲透一个行业，输出图文并茂的深度分析 Markdown 报告，保存到当前项目的 markdown/ 目录。触发条件：用户输入一个行业名称，并希望获得全面、通俗易懂的行业深度分析，关键词包括"讲透"、"深度分析行业"、"行业分析"、"帮我搞懂XX行业"、"XX行业是怎么赚钱的"、"XX行业有什么机会"、"XX行业怎么进入"、"产业链分析"、"行业机会"等。即使用户只说"帮我了解一下XX行业"或"XX行业值得进入吗"，也应使用本 skill。输出报告覆盖：产业链分布与利润图谱、供给-需求-连接分析、人货场分析、代表企业与产品分析、产业链不合理之处、机会推演与进入策略（产品/服务/目标客户/商业模式/定价/市场规模）、竞争对手与合作伙伴分析，要求逻辑清晰、有理论依据、有历史数据/事件支撑、图文并茂（SVG/Mermaid/ASCII图）。
daily-finance
by digoalGenerate stage-1 publishable daily financial news markdown by accessing current web data, filtering reliable macro/market news, validating numbers and article logic, and writing a source-backed brief. Use when user asks for "今日财经", financial summary, market analysis, daily finance report, 公众号财经日报, or the first step of the daily finance pipeline that feeds finance-core-analysis and finance-explosive-article.
database-foundation-course-writer
by digoalWrite Chinese "数据库筑基课" Markdown articles for database architects, DBAs, and application developers. Use when the user provides a database foundation article title and references such as technical docs, product manuals, open-source repositories, DeepWiki pages, papers, source code, or related blog posts, and wants a rigorous, SVG-rich, practice-oriented GitHub-renderable Markdown article saved as a .md file.
digoal-perspective
by digoaldigoal/德哥的思维框架与表达方式。基于本地 digoal/blog 长文、ASK/访谈、 技术课程、公开履历、社区活动页和近年 AI skill 行动记录的调研， 提炼5个核心心智模型、8条决策启发式和表达DNA。 Use when the user explicitly asks for 德哥/digoal perspective, judgment, advice, analysis, or expression style, including requests such as 「用德哥的视角」「digoal会怎么看」「按德哥的思路拆一下」 「德哥会怎么写」「用德哥风格改写」「digoal perspective」 or 「切换到德哥」. Apply it to database practice, technical communication, open-source ecosystems, AI skillization, product opportunities, career learning, and cross-domain judgment.
digoal
by digoalPortable digital employee distilled from digoal's personal blog for PostgreSQL, PolarDB, DuckDB, AI+database, vector/RAG, database operations, source-code reading, technical content creation, open-source community strategy, and "德说" style strategic analysis. Use when asked to answer as 德哥/digoal, mine a local digoal/blog checkout for database expertise, write or review PostgreSQL/AI database articles, design database solutions, troubleshoot PG/PolarDB problems, interpret commits or papers for DBAs, or turn knowledge into reusable AI skills. Works inside blog/skills/digoal or as a copied skill in another AI agent when DIGOAL_BLOG_ROOT or --blog points to the local blog checkout.
douban-book-notes
by digoalGenerate a source-backed, shareable Chinese Markdown reading note from a Douban book URL. Use when the user provides a Douban book link and asks for 读书笔记, book notes, book summary, reading reflection, 图文并茂笔记, or a Markdown article that combines book metadata, high-quality external research, the author's argument, evidence, logic, assumptions, insights, transferability, and personal reflection saved under the current project's markdown directory.
industry-deep-explainer
by digoal系统性'讲透'一个行业的深度分析技能。覆盖产业链分布、利润分布、代表企业、人货场、供需连接, 并推演不合理点、机会点、入局策略(产品/客群/商业模式/定价)、竞争对手与合作伙伴。触发场景: 讲透/分析XX行业、XX产业链、XX行业机会、如何进入XX行业、XX赛道分析、我想做XX怎么切入等。输出图文并茂的深度分析报告到当前项目 markdown/ 目录。
rational-red-blue-debate
by digoalAnswer general or cross-domain questions with a non-pleasing rational mode: adversarial red-team and blue-team expert analysis, mutually exclusive conclusions, up to five debate rounds, saved Markdown intermediate expert outputs, and a final first-person judge-written plain-language article. Use when the user asks for rational adversarial analysis, red/blue team debate, non-flattering judgment, evidence-backed multi-expert reasoning, or a final Markdown answer to a broad question.
daily-finance
by digoalGenerate stage-1 publishable daily financial news markdown by accessing current web data, filtering reliable macro/market news, validating numbers and article logic, and writing a source-backed brief. Use this skill whenever the user asks for "今日财经", "财经日报", "财经简报", "市场要闻", "每日财经", financial summary, market analysis, daily finance report, 公众号财经日报, or the first step of the daily finance pipeline that feeds finance-core-analysis and finance-explosive-article. Even if the user only says "帮我看看今天市场怎么样" or "今天有什么财经新闻", use this skill.
db-foundation-course
by digoal编写"数据库筑基课"文章的专用 Skill，面向数据库架构师、DBA 和业务开发者。输入一个筑基课文章标题及相关参考资料（技术文档、产品手册、开源项目地址、deepwiki、论文等），输出图文并茂、结构完整的 Markdown 筑基课文章，保存至项目 markdown/ 目录。适用主题覆盖：表存储结构（heap/parquet/arrow/zedstore/LSM-Tree/HStore等）、索引结构（btree/gin/brin/hnsw/ivfflat/bloom等）、数据类型与操作符（vector/jsonb/tsvector/range/array等）、优化器扫描算法（seq scan/index scan/bitmap scan/join/agg/并行等）、场景化实践（时序/GIS/RAG/全文检索/图数据/数据湖等）以及事务/锁/并发/安全等周边主题。只要用户提及"筑基课"、"数据库原理"、"数据库内核"写作，或给出了数据库存储/索引/扫描相关的技术文档让你写成教程，都应使用此 Skill。
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.