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...
spoken-english-coach
by wangjs-jacky英语口语表达教练。当用户想要提升英语口语表达、翻译中文到英文口语、建立个性化表达库、处理口述文章、或询问"这个用英文怎么说"时触发此 skill。
doc-to-tutorial
by wangjs-jacky将任意内容（文件夹/文件/文字）转换为交互式教程并启动预览服务。触发词：文档转教程、生成交互式教程、创建教程、制作教程、tutorial、interactive tutorial、转教程、做教程、写教程、文档变教程、把文档做成教程
repo-study
by wangjs-jacky研究 GitHub 仓库的特定技术实现。触发词：调研下、研究下、学习下、看看 xxx 仓库、分析开源项目、repo-study
parallel-translation
by wangjs-jacky智能翻译调度器：自动判断单文件/多文件，使用 haiku 模型低成本翻译。触发词：翻译、translate、多文件翻译、仓库翻译、中文翻译
tt-worker
by wangjs-jacky读取滴答清单任务池中的任务并自动执行。触发词：执行任务池、tt-worker、处理待办、跑任务池、自动执行。
tt-defer
by wangjs-jacky将任务推送到滴答清单任务池，支持自然语言触发和命令触发。触发词：推到待办、丢到池子、明天再做、tt-defer、推任务、推迟任务。
tt
by wangjs-jackyTickTick 日程管理。触发词：/tt、日程、待办、计划、任务、滴答清单、日程复盘、时间都去哪了、今天干了什么、补全日程、回顾今天。
llm-wiki
by wangjs-jackyLLM 驱动的 Obsidian 个人知识库管理。当需要评估、优化、索引 Obsidian vault，或讨论知识管理、wiki 构建、LLM 辅助笔记、索引优先检索时触发。
cc-history
by wangjs-jacky查询 Claude Code 会话历史记录。触发词：cc-history、CC 历史、今天做了什么、昨天做了什么、工作记录、会话记录。
j-skills
by wangjs-jackyCLI tool for managing Agent Skills - link, install, and manage skills across 35+ coding agent environments. Use when user needs to manage skills, link local skills, or install skills to environments.
sounding-board
by wangjs-jacky第三视角谈话型思考导师 - 当你带着一团模糊的困惑（工作/生活/学习/情感/某个想法）想理清时启动。它不急着帮你解决，而是先陪你把『真正的问题』讨论出来，再以苏格拉底式诘问抽丝剥茧，并把每次对话沉淀为可追溯、会演化的结构化思考日志（假设台账 + 知识全景图）
skill-researcher
by wangjs-jacky研究 Claude Code Skills 的元技能。当用户想要研究、对比、分析 GitHub 上的 Skills 项目时使用。支持搜索热门项目、下载翻译、生成对比报告。触发词：研究 skills、对比 skills、分析 skill、下载 skill。
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.