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...
job-article
by itwanger根据指定选题，按照二哥的写作风格完成求职/校招/面试/职场类文章撰写。专注于秋招春招建议、公司薪资爆料+学习路线、面经八股解析、求职心态与球友故事分享。触发关键词：写一篇求职文章、秋招、春招、校招、offer、面经、薪资、面试、八股、简历、求职建议、球友故事、学习路线等。
multi-platform-publisher
by itwangerPublish local Markdown articles from the toBeBetterJavaer workflow to multiple authenticated writing platforms with Chrome, especially CSDN, Juejin, and Bilibili opus/专栏. Use when the user asks to sync, post, publish, cross-post, or create drafts from a Markdown article on platforms such as CSDN, 掘金/Juejin, B 站/Bilibili, 知乎, 博客园, or similar sites, and when final status must be verified from the resulting article page rather than assumed from editor saves.
title-generator
by itwanger为公众号文章生成5个高打开率候选标题。当用户写完文章需要起标题、要求生成标题、优化标题、或者说"帮我想几个标题"时使用此 Skill。也适用于用户提供文章主题/关键词后要求生成标题的场景。触发关键词包括：标题、起标题、取标题、想标题、title、爆款标题、打开率。
zhihu-search
by itwangerSearch Zhihu for content using the search_v3 API. Use when user mentions Zhihu, 知乎, searching Zhihu, searching 知乎, Zhihu search, 知乎搜索, searching for answers/articles on Zhihu, or says 搜一下知乎, 在知乎上搜索, 找知乎上的, 知乎上搜搜. Also applies to exploring Zhihu's internal API endpoints, extracting search results in bulk, and monitoring Zhihu for new content on specific topics.
ai-article
by itwanger用于AI类内容的撰写。支持三种风格：安装教程类（手把手教学）、产品评测类（有观点有数据）、面试对话类（面试场景）。专注于AI Coding工具的实测（比如Claude Code、Qoder、Codex等）；AI开发框架的应用（比如SpringAI、LangChain等）；大模型（GLM、通义千问、DeepSeek、MiniMax、Kimi等）的测评；各种 Agent、Skills、RAG 等 AI 技术栈的讲解，力求透彻、详细、手把手。
ai-podcast
by itwanger生成专业播客脚本，包含开场白、话题展开、问答环节和结尾。适用于创建音频内容、播客脚本、对话式内容。
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.