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...
skills-backup
by wsxwj123Use when the user asks to backup or sync their skills. This skill automatically detects the OS (Windows/Mac) and syncs the entire skills directory to the configured Git repository, handling cross-platform conflicts like line endings and paths.
general-skills-backup
by wsxwj123Use when the user asks to backup or sync their Codex skills. This skill auto-detects Windows/macOS and syncs the whole skills directory to the user's configured Git remote, handling cross-platform line ending and path differences.
medical-imaging-review
by wsxwj123Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests for "review paper", "survey", "literature review", "综述", "systematic review", or mentions of writing academic reviews on deep learning for medical imaging.
fetch-everything
by wsxwj123尽可能快速、准确、完整地获取网页内容，并默认输出 Markdown。只要用户提到抓取网页、提取文章正文、读取文档页面、处理微信公众号链接、转换网页为 Markdown、获取在线内容、提取页面关键信息，或遇到反爬/动态页面需要多种回退方案时，都应使用此技能。优先按站点类型智能分流：在线转换服务、直接抓取、Scrapling、本地浏览器自动化依次协作。
read
by wsxwj123Invoke whenever the user's message contains any http(s) URL, web page link, or PDF path, even if the user only says "analyze", "summarize", "look at", or "what does X say". Always prefer this skill over WebFetch for any URL. WebFetch is not a substitute and fails on X/Twitter, paywalls, and auth-gated pages. Not for local text files or source code already in the repo.
social-clip
by wsxwj123从社交平台链接提取完整内容并保存到 Obsidian 知识库。 支持平台：小红书（图文帖/视频帖）、B站、微博、抖音、YouTube、Twitter 等。 只要用户分享社交/视频平台链接（无论是否附带文字），都自动触发。 也适用于直接粘贴短链（xhslink.com、b23.tv 等）的场景。 除非用户明确说"看看就好"、"不用"等否定词，否则不需要确认。 核心承诺：不跳过任何图片、不压缩转写内容、每个要点/例子/数据都完整保留。
moebius-style-art-prompt
by wsxwj123Use when users ask to generate, expand, or rewrite image prompts into Moebius-style/清线描 fantasy concept art with strict bilingual plaintext blocks, fixed section structure, camera-angle vocabulary constraints, and mandatory keyword injection.
brand-guidelines
by wsxwj123Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
learn
by wsxwj123Invoke when diving deep into an unfamiliar domain, preparing a research article, or turning collected sources into publish-ready output. Runs a six-phase workflow: collect, digest, outline, fill in, refine, self-review. Not for quick lookups or single-file reads.
reviewer-response-sci
by wsxwj123用于 SCI 审稿意见逐条回复的全流程技能，适用于期刊大修/小修阶段，输入论文正文与审稿意见，输出含双栏 HTML 导航、中英文对照、修改定位的完整回复包。当用户提到「审稿意见回复」「回复审稿人」「回复reviewer」「修回」「修稿」「Response to Reviewer」「revise and resubmit」「R&R」「reviewer comments」时优先调用。注意与 reviewer-simulator（模拟写审稿意见）区分：本技能是针对已收到的审稿意见撰写回复。
meeting-insights-analyzer
by wsxwj123Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.
tailored-resume-generator
by wsxwj123Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
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.