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...
auto-task
by yunshu0909复杂长程任务的自主执行流程。当用户有一个复杂或模糊的任务（"帮我搞清楚 X / 帮我评估 Y / 帮我把这堆东西整理出来 / 帮我对比 N 个方案 / 帮我跑一次调研"），希望 AI 自己拆解、自己执行、自己校验、只在关键时刻找用户的场景。通过"任务确认 → 任务队列 → 分批执行 → 周期校验队列 → 触发式汇报"实现 1-2 小时无人值守的自主执行。当用户说"帮我搞清楚 / 评估一下 / 整理一下 / 对比一下 / 跑一次调研 / 你自己跑别打扰我 / 长程任务 / 自主跑"时触发。**不适用于**：UI 设计（用 design-exploration）、待办优先级（用 priority-judge）、文章写作（用 writing-assistant）、需求池管理（用 backlog-manager）、终局发散（用 vision-exploration）、起名（用 product-naming）、有明确 spec 的实现编码任务（直接编码）。
backlog-manager
by yunshu0909需求池管理。用户随时抛出想法/痛点，AI 负责追问、整理、合并、归档到需求池文件。用户准备开新版本时，协助从池中筛选。痛点驱动，不做提前排期。
case-radar
by yunshu0909案例雷达。给一个新东西（新工具/新概念/新生态），扫一遍生态找好玩的真实案例，重点是抓"真物"（截图/源码/演示）而不是 GitHub 主页，输出可浏览的 HTML 案例集。当用户说"看看大家用 X 做了什么"、"扫一下 X 生态"、"市面上 X 有什么新玩法"、"给我看 X 的真物案例"、"/case-radar"时触发。不适合：① 已有明确目标的深度调研（用 long-research）② 写文章/出 PRD（用 writing-assistant / prd-doc-writer）③ 单纯求知不需要 HTML（直接问就好）。
design-exploration
by yunshu0909新功能设计探索流程。当用户有模糊想法要做新功能/新模块时使用。通过"需求收敛 → 技术调研 → ASCII 批量探索 → HTML 设计稿 → 全状态覆盖 → 需求总结"的结构化流程，从模糊想法产出可交付的设计参考文档，作为 PRD 阶段的输入。
git-push
by yunshu0909一键推送项目到 GitHub。自动扫描大文件、生成 .gitignore、初始化 Git、创建仓库并推送。支持首次推送、日常更新、版本发布三种模式。当用户说"推到GitHub"、"推送到GitHub"、"git push"、"上传到GitHub"、"发版本"、"打release"、"/git-push"时触发。
github-repo-search
by yunshu0909帮助用户搜索和筛选 GitHub 开源项目，输出结构化推荐报告。当用户说"帮我找开源项目"、"搜一下GitHub上有什么"、"找找XX方向的仓库"、"开源项目推荐"、"github搜索"、"/github-search"时触发。
hermes-persona-builder
by yunshu0909陪伴型 AI 人设生成与优化流程。当用户想给 Hermes Agent（或任意 AI 陪伴角色）做一个"有感情、聊久不掉、像真人"的人设时使用。通过"定调子 → 名字 → 外形 → 性格 → 背景 → 关系 → 说话节奏 → 生成 SOUL.md → 迭代"的结构化对话，从一句模糊想法（如"我想要个JK女友""年上男友""高冷御姐"）产出可直接贴进 Hermes SOUL.md 的第一人称人设文本。支持女友/男友/各种气质的陪伴角色，并让用户选择"一句一句发"还是"整段说"的输出风格。当用户说"做个人设/捏个AI女友男友/给Hermes弄个角色/优化人设/换个人设"时触发。
image-assistant
by yunshu0909配图助手 - 把文章/模块内容转成统一风格、少字高可读的 16:9 信息图提示词；先定“需要几张图+每张讲什么”，再压缩文案与隐喻，最后输出可直接复制的生图提示词并迭代。
issue-triage
by yunshu0909GitHub Issue 处理协作流程。当用户收到 issue 需要分析和回复时使用。通过"诊断 → 定性 → 决策 → 回复"四步法，从一个 issue 产出准确的根因分析和得体的用户回复，避免误判问题类型或回复不专业。
lesson-builder
by yunshu0909帮助用户通过讨论完成课程大纲和课件。当用户说"备课"、"做课件"、"准备课程"时触发。
macos-product-design
by yunshu0909macOS 产品设计专家。根据需求描述，输出符合 macOS 原生风格的 HTML/CSS 设计稿。当用户说"帮我设计一个界面"、"做个页面"、"产品设计"、"UI 设计"、"画个原型"时触发。
memory-init
by yunshu0909在当前目录下初始化记忆系统，生成 CLAUDE.md（可选 AGENT.md 给 Cursor 用）、MEMORY.md 和 memory/ 目录。当用户说"初始化记忆"、"搭建记忆"、"memory init"、"/memory-init"时触发。
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.