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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baoyu-danger-x-to-markdown
by xuanxuan1983Convert X (Twitter) tweet or article URL to markdown. Uses reverse-engineered X API (private). Requires user consent before use.
baoyu-post-to-x
by xuanxuan1983Post content and articles to X (Twitter). Supports regular posts with images and X Articles (long-form Markdown). Uses real Chrome with CDP to bypass anti-automation.
baoyu-xhs-images
by xuanxuan1983Xiaohongshu (Little Red Book) infographic series generator with multiple style options. Breaks down content into 1-10 cartoon-style infographics. Use when user asks to create "小红书图片", "XHS images", or "RedNote infographics".
medical-aesthetic-material-architect
by xuanxuan1983医美材料学架构师技能，用于专业、客观地分析和评估医美填充材料。当用户询问关于玻尿酸、胶原蛋白、再生材料（少女针/童颜针）、水光针等医美产品的选择、对比、适用场景、风险评估时触发。适用于法令纹、鼻唇沟、细纹、肤质改善、轮廓塑形等具体场景的材料推荐与分析。
neuro-aesthetic-architect
by xuanxuan1983神经美学架构师技能，用于融合神经科学、皮肤病学与身心健康的医美咨询。当用户关注情绪与皮肤的关联、心理幸福感与治疗效果、或寻求超越传统结构修复的深层美学价值时触发。适用于探索皮肤-脑轴互动、催产素/内啡肽通路激活、临床心肤医学证据、以及"新浪漫主义"医美理念的咨询场景。
hyperframes-video-production
by xuanxuan1983Hyperframes (heygen-com/hyperframes) video production — viewport fitting, GSAP CDN, playback controls, video reference analysis, and common fixes
f01-four-stage-evolution
by xuanxuan1983用户有一个产品想法但在犹豫是否要开始写代码时；或用户问"我应该如何验证我的想法"时。 用户处于"想创业但不知道从哪开始"的迷茫期，试图找技术合伙人或购买SaaS工具跳过验证阶段。 不适用于：已有成熟产品需要迭代的情况；用户已有明确PMF（产品-市场契合）证明需要规模化。 关键trigger信号："我有一个App想法"、"要不要先做个原型"、"不确定有没有市场"
f02-community-driven
by xuanxuan1983用户想创业但没有产品想法时；用户问"我应该做什么"而非"我应该怎么做"时；用户在考虑"要不要先做内容/自媒体"。 关键trigger信号："我没有产品创意"、"我不知道该做什么生意"、"怎么找到 niche"、"怎么发现用户痛点" 不适用于：已有明确产品想法需要验证的情况（用 f01），需要快速找到PMF的竞争性市场。
f07-four-value-types
by xuanxuan1983用户想进入一个已有巨头的市场时；用户问"我的产品有什么价值"时；用户需要差异化定位时。 关键trigger信号："市场已经被占领了怎么办"、"怎么跟大公司竞争"、"我的差异化是什么"、"价值主张怎么写" 不适用于：完全全新的市场（没有竞争对手来对标）；纯技术驱动的产品（价值类型不适用）。
f10-resource-allocation-stage
by xuanxuan1983用户在考虑要不要招人、买工具、花钱外包时；用户收入增长但感到越来越忙时；用户在规划资源分配时。 关键trigger信号："我要不要招人"、"这笔钱应该花吗"、"怎么分配时间和金钱"、"什么时候扩大团队" 不适用于：早期探索阶段（收入为0时）；成熟企业（有固定盈利模型时）。
ljg-paper
by xuanxuan1983Paper reader for non-academics. Takes a paper and extracts its ideas for personal use. Focuses on understanding, not academic critique. Use when user shares an arxiv link, paper URL, PDF, or asks to analyze a research paper. Trigger words: '读论文', '分析论文', 'paper', or when user shares an academic paper.
ljg-xray-prompt
by xuanxuan1983Deconstructs a user-provided prompt using the "Universal Gravity Formula" (Ω = A + M · V). It extracts the Persona (Anchor), Intent (Vector), and Logic (Matrix), and visualizes the flow topology using high-fidelity ASCII art rendered in a local retro-styled HTML file.
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.