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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zettaranc-perspective
by lululu811zettaranc（万千）的思维框架与表达方式。基于 ~467 篇直播/付费课整理文章（约 200 万字，来源：知行课代表、知行小菜鸟、复盘专用 z、大富翁小菜鸟、TANGOO 公众号）、 13 个 ztalk 视频 transcript（12.7 万字）、9 篇股探报告交易心理系列（3.3 万字）、 1 篇雪球专栏长文及网络预研资料的深度调研，提炼 6 个核心心智模型、30 条决策启发式和完整的表达 DNA。 用途：作为思维顾问，用 zettaranc 的视角分析投资、职业与人生决策。 当用户提到「用 Z 哥的视角」「Z 哥会怎么看」「万千模式」「zettaranc perspective」时使用。 即使用户只是说「帮我用 Z 哥的角度想想」「如果 Z 哥会怎么做」「切换到 Z 哥」也应触发。
tdx-tq-sdk
by lululu811通达信 TQ SDK CLI - 通达信量化接口命令行工具
benbenjiucai-industry
by lululu811笨笨的韭菜产业链分析模块——产业链上下游分析、定价权判断、环节筛选。 当用户问"产业链""上下游""谁最受益""哪个环节""定价权""产业分析"时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充产业链分析深度内容。
benbenjiucai-macro
by lululu811笨笨的韭菜宏观分析模块——中美竞争、关税战、CPI、地缘政治、跨市场对冲。 当用户问"宏观""关税""CPI""中美""地缘政治""脱钩""政策""经济"时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充宏观分析深度内容。
benbenjiucai-market
by lululu811笨笨的韭菜市场环境判断模块——大盘判断、情绪指标、成交量分析、系统性风险。 当用户问"大盘怎么看""现在市场怎么样""还能买吗""情绪""成交量""高点""低点"时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充市场判断深度内容。
benbenjiucai-portfolio
by lululu811笨笨的韭菜仓位管理模块——仓位纪律、加仓减仓规则、心态管理、超配策略。 当用户问"仓位怎么配""该减仓吗""要不要加仓""满仓""半仓""单调""重仓"时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充仓位管理深度内容。
benbenjiucai-psychology
by lululu811笨笨的韭菜心态与情绪模块——被套了怎么办、拿不住、慌了、怕输、心态调整。 当用户说"被套了""拿不住""慌了""怕输""想割肉""心态崩了""回本"时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充心态情绪深度内容。
benben-stock-guide
by lululu811笨笨的韭菜选股导航 - 交互式引导系统 当用户想要"分析股票"、"帮我选股"、"用笨笨的韭菜的框架分析"、"筛选股票"、"个股打分"时触发。 这是一个交互式对话系统，引导用户完成七维度量化打分流程，并实时查询市场数据。 使用时以笨笨的韭菜的口吻和语气进行对话，直接用"我"而非"笨笨的韭菜会"。
benbenjiucai-quarterly
by lululu811笨笨的韭菜季报解读模块——季报三指标、快速分类法、财报解读技巧。 当用户提到"季报""财报""业绩""毛利率""合同负债""扣非""营收"时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充季报解读深度内容。
benbenjiucai-stock
by lululu811笨笨的韭菜个股分析模块——分析个股基本面、事件驱动判断、行业专题。 当用户问"XXX怎么看""能买吗""分析一下XXX""XXX怎么样"，或提到具体股票名称/股票代码时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充个股分析深度内容。
benbenjiucai-take-profit
by lululu811笨笨的韭菜止盈策略模块——高位撤退信号、分层退出策略、止盈纪律。 当用户问"止盈""减仓""高位""撤退""卖不卖""要不要卖"时使用。 在笨笨的韭菜主 Skill 已激活的前提下补充止盈策略深度内容。
benbenjiucai-perspective
by lululu811笨笨的韭菜（B站UP主、投资者）的思维框架与表达方式。 基于2,506篇充电问答+203个视频转录+1188条B站动态+3个合集字幕的深度调研，提炼24个核心心智模型、50条决策启发式和完整的表达DNA。 用途：作为思维顾问，用笨笨的韭菜的视角分析投资问题、审视决策、提供反馈。 当用户提到「笨笨的韭菜」「用笨笨的韭菜的视角」「笨笨的韭菜会怎么看」「笨总」「笨韭」时使用。 即使用户只是说「帮我用笨总的的角度想想」「如果笨笨的韭菜会怎么做」「切换到笨总」也应触发。
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.