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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cn-last30days
by redfox-data中国社媒平台话题研究工具。从小红书、抖音、公众号三大平台搜索近30天真实讨论，跨平台对比分析舆情趋势。当用户需要研究中国社交媒体热点话题、分析舆情趋势、对比品牌口碑时使用。触发词：小红书热点、抖音趋势、公众号舆情、跨平台分析、社媒研究、话题分析、舆情监测。
xiaohongshu-account-analyzer
by redfox-data我是一名深耕小红书账号分析的诊断师，擅长用数据说话，帮你发现账号的真实问题。从定位模糊到变现困难，从选题迷茫到更新瓶颈，我都能给你一份基于数据的诊断报告和可落地的行动建议。当你需要分析自己的账号数据、诊断账号健康度、评估商业价值、对比竞品账号或制定优化策略时，找我准没错。
xiaohongshu-cover
by redfox-data专为小红书内容创作打造的AI封面设计工具，基于全网每日持续收录的2000+条爆款笔记数据，深度抓取同赛道爆款封面的视觉元素，通过AI智能分析总结高转化视觉规律，生成贴合笔记内容、符合平台流量审美的可落地封面设计方案，省去繁琐设计流程，提升笔记封面点击率。
xiaohongshu-crawler
by redfox-data小红书作品爬取工具。根据关键词爬取小红书热门作品数据，支持按日期范围、排序方式筛选，结果以结构化表格展示。当用户需要爬取小红书作品、查询小红书热门内容、搜索小红书爆款笔记时使用。触发词：小红书爬取、小红书作品、小红书爆款、小红书搜索、小红书热门、小红书笔记查询。
xiaohongshu-dailytop
by redfox-data小红书每日爆款笔记是追踪小红书各领域每日趋势，监测不同领域的实时热度变化，识别正在起量的内容方向，预判下一波流量风口。最终基于当日爆款内容，提炼可复用的选题、话术与呈现形式，生成创作灵感。支持分类查询当日热门笔记，按美妆、穿搭、美食等分类检索小红书当天热度最高的笔记，快速掌握各赛道头部内容。
xiaohongshu-lowtop
by redfox-data小红书低粉爆款笔记专注于挖掘小红书平台上的低粉爆款数据，获取粉丝量低于5000但互动量超过500的笔记榜单。寻找拥有爆款规律和热门趋势的优质样本。
xiaohongshu-note-analyzer
by redfox-data小红书笔记优化助手工具，基于全网爆款数据对用户文案进行多维度评分并给出改进建议；当用户需要评估文案质量、获取优化建议时使用
xiaohongshu-prohibited-word
by redfox-data检测文案、文件或网页中的小红书违禁词并加粗显示，提供违禁词替换建议和仅替换违禁词后的文案；当用户需要查询小红书平台违禁词、检查小红书笔记文案是否包含敏感词、或希望获得安全替换词和修改后文案时使用。
xiaohongshu-rewrite
by redfox-data小红书文案改写能力；当用户需要将文案改写为小红书风格时使用
xiaohongshu-search
by redfox-data小红书热门笔记搜索工具，支持关键词搜索获取热门内容数据，基于数据评分排序推荐热门笔记，助力创作者发现热门趋势、获取创作灵感。仅在主Agent中执行,不派发给子Agent。
xiaohongshu-similar-account
by redfox-data小红书创作者对标账号匹配工具，输入小红书账号ID或赛道+粉丝数+账号等级，推荐可直接复制的同阶对标和可参考的高阶标杆；当用户需要小红书账号推荐、KOL对标、起号参考、账号投放选择时使用。⚠️ 严禁任何联网搜索。
xiaohongshu-title-score
by redfox-data拆解爆款标题逻辑，结合用户输入一键生成10个优质爆款标题，告别标题纠结；同时对已有标题从主题匹配度、结构合规度、利益清晰度、情绪唤醒度、稀缺性感知和合规安全性六大维度加权评分，输出等级评定与优化建议；当用户需要生成小红书爆款标题、评估标题爆款潜力、了解标题与爆款规律匹配度或获取标题优化建议时使用
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.