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...
xhs-explore
by autoclaw-cc小红书内容发现与分析技能。搜索笔记、浏览首页、查看详情、获取用户资料。 当用户要求搜索小红书、查看笔记详情、浏览首页、查看用户主页时触发。
xhs-publish
by autoclaw-cc小红书内容发布技能。支持图文发布、视频发布、长文发布、定时发布、标签、可见性设置。 当用户要求发布内容到小红书、上传图文、上传视频、发长文时触发。
xhs-interact
by autoclaw-cc小红书社交互动技能。发表评论、回复评论、点赞、收藏。 当用户要求评论、回复、点赞或收藏小红书帖子时触发。
xhs-auth
by autoclaw-cc小红书认证管理技能。检查登录状态、登录（二维码或手机号）、退出登录。 当用户要求登录小红书、检查登录状态、退出登录时触发。
xhs-content-ops
by autoclaw-cc小红书复合内容运营技能。组合搜索、详情、发布、互动等能力完成运营工作流。 当用户要求竞品分析、热点追踪、内容创作、互动管理等复合任务时触发。
xhs-login
by autoclaw-cc管理小红书登录状态：检查是否已登录、二维码扫码登录、重置登录切换账号。 当用户提到登录、扫码、账号、切换账号、退出登录、登录状态检查，或其他 skill 报告"未登录"需要先登录时使用。
xiaohongshu
by autoclaw-cc小红书（RED/XHS）自动化助手。提供完整的小红书操作能力：登录、发布图文/视频、搜索笔记、浏览详情、点赞收藏评论、查看博主主页、内容策划。 当用户提到小红书、红书、XHS、RED、发笔记、搜笔记、小红书运营等任何与小红书相关的操作时使用此 skill，即使用户没有明确说"小红书"但描述的场景明显是小红书（如"发一篇种草笔记"、"帮我分析这个博主"）也应触发。
xhs-content-plan
by autoclaw-cc小红书内容策划助手：搜索分析热门内容和竞品，帮助规划内容方向、选题、标签策略。 当用户想做小红书运营规划时使用——内容策划、选题灵感、竞品分析、爆款分析、热门话题研究、怎么做小红书、涨粉策略等。
xhs-explore
by autoclaw-cc浏览小红书推荐流、查看笔记详情和评论。 当用户想看推荐内容、刷首页、查看某条笔记的详情/评论、或已有 feed_id 想获取完整内容时使用。
xhs-interact
by autoclaw-cc对小红书笔记进行互动：点赞/取消点赞、收藏/取消收藏、发表评论、回复评论。 当用户想对小红书笔记进行互动时使用——赞一下、收藏一下、留个评论、回复某条评论、取消点赞、取消收藏等。
xhs-profile
by autoclaw-cc查看小红书用户主页：基本信息、粉丝/关注/获赞数据、发布的笔记列表。 当用户想查看某个博主、作者、用户的主页信息和作品时使用。
xhs-search
by autoclaw-cc搜索小红书笔记，支持关键词搜索和多维度筛选（排序、内容类型、时间范围、位置等）。 当用户想在小红书上搜索、查找内容时使用——包括搜笔记、找攻略、看看小红书上有没有某某内容、搜一下、查一查等场景。
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.