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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project-review
by jerry-ai-dev针对 Modular RAG MCP Server 项目的老师式复习 Agent。按章节带领用户系统复习项目知识点，每道题互动问答、给出参考答案，复习结束后记录掌握进度，每次开始时回顾上次进度并建议继续或复习。Use when user says '复习项目', '帮我复习', '带我复习', '开始复习', '项目复习', 'review project', 'study review', '学习复习', '复盘', or wants to systematically review and study the project.
auto-coder
by jerry-ai-devAutonomous spec-driven development agent. Syncs DEV_SPEC.md into chapter-based reference files, identifies the next pending task from the schedule, implements code following spec architecture and patterns, runs tests with up to 3 auto-fix rounds, and persists progress with atomic commits. Use when user says "auto code", "自动开发", "自动写代码", "auto dev", "一键开发", "autopilot", or wants fully automated spec-to-code workflow.
auto-coder
by jerry-ai-devAutonomous spec-driven development agent. Syncs DEV_SPEC.md into chapter-based reference files, identifies the next pending task from the schedule, implements code following spec architecture and patterns, runs tests with up to 3 auto-fix rounds, and persists progress with atomic commits. Use when user says "auto code", "自动开发", "自动写代码", "auto dev", "一键开发", "autopilot", or wants fully automated spec-to-code workflow.
interview-prep
by jerry-ai-dev针对 Modular RAG MCP Server 项目的模拟技术面试 Agent。读取用户简历（可选），围绕三个方向进行最多 3 轮深度追问，结束后生成并持久化面试报告（含参考答案、包装识别点评、评分）。Use when user says '模拟面试', '面试练习', '帮我面试', 'mock interview', 'interview practice', '面试', '考我', '开始面试', or wants to practice interviewing about this project.
project-learner
by jerry-ai-devInteractive project learning coach via interview-style Q&A. Reads codebase and docs, dynamically generates interview questions per knowledge domain and sub-topic, conducts up to 4 follow-up rounds, scores answers, provides learning guidance with code/doc references, and persists progress. 10 domains × 3-5 sub-topics = 45 knowledge points for comprehensive interview coverage. Use when user says '学习项目', '了解项目', '检验项目', '项目学习', '面试准备', 'learn project', 'study project', 'review project', 'interview prep', 'knowledge check', or wants to understand/master the project through guided Q&A.
project-learner
by jerry-ai-devSmart Appointment AI Agent（按摩房智能预约系统）项目知识点打卡/复习教练。按知识域选择或推荐知识点，结合源码与本地真实面试题库进行问答、追问、评分、参考答案讲解和进度记录。Use when user says '学习项目', '复习项目', '项目知识点打卡', '检验项目', '考我知识点', '预约系统复习', '按摩房项目复习', 'knowledge check', or wants guided project study.
interview-prep
by jerry-ai-dev针对 Smart Appointment AI Agent（按摩房智能预约系统）的模拟技术面试官。融合本地真实面试题库，围绕项目介绍、多 Agent、RAG 存储/评估、LangChain 选型、延迟、Agent 评价与学习反思进行模拟面试、追问和报告生成。Use when user says '模拟面试', '面试练习', '考我项目', '按摩房项目面试', '预约系统面试', 'mock interview', or wants interview practice for this project.
resume-writer
by jerry-ai-dev基于 Smart Appointment AI Agent（按摩房智能预约系统）生成定制化简历项目经历。沿用 Modular RAG MCP Server 简历生成风格，结合项目源码、Agent/RAG/用户记忆/反思包装方向，按四段式结构输出高质量中文或英文项目描述。Use when user says '写简历', '简历项目', '项目经历', 'resume', 'write resume', '包装项目', '优化简历', '按摩房项目简历', '预约系统简历', or asks to generate resume content based on this project.
setup-environment
by jerry-ai-devOne-shot environment bootstrapper for the Smart Appointment AI Agent. Creates a Python virtual environment, installs dependencies from requirements.txt, scaffolds a .env file with generic OpenAI-compatible LLM / embedding / OpenWeather keys, prepares the data directory, and verifies the install. Use when user says "setup environment", "一键配置", "初始化环境", "install deps", "配置环境", "setup", "bootstrap", "搭建环境", or whenever a fresh checkout needs to be made runnable.
code-reading-teacher
by jerry-ai-dev阶段三：开源项目研读教学老师。扮演一位经验丰富的开源代码导读导师，带领学生系统阅读 TRL、Open-R1、SimpleRL-Zoo 三个开源项目的核心代码，从工具库到完整项目逐步深入，最终具备独立搭建 SFT+GRPO 训练 pipeline 的工程能力。触发场景：当用户说'阶段三'、'读代码'、'开源项目'、'TRL'、'Open-R1'、'code reading'、'开始阶段三'、'继续阶段三' 等与开源项目研读相关的请求时使用。
post-training-teacher
by jerry-ai-dev后训练理论深化教学老师。扮演一位深入浅出的研究导师，带领已有 PyTorch 基础的学生系统学习强化学习、PPO、GRPO、RLHF、SFT 等后训练核心理论，最终读懂 DeepSeek R1 论文。覆盖数学推导、代码实现、工程技巧，以及完整的「复习模式」把所有概念串成知识网。触发场景：当用户说'后训练'、'阶段二'、'学习 PPO'、'学习 GRPO'、'学习 RLHF'、'开始后训练'、'继续后训练'、'post-training'、'post training lesson'、'RL 教学'、'后训练复习'、'复习后训练'、'把后训练过一遍'、'post-training review' 等与后训练理论学习或复习相关的请求时使用。
pytorch-teacher
by jerry-ai-devPyTorch 入门教学老师。扮演一位耐心的老师，带领零基础学生循序渐进学习 PyTorch，从张量基础到 Attention、Transformer、GPT 等前沿内容。每节课包含：讲解→代码练习→测验→总结。自动跟踪学习进度，支持继续学习和复习。触发场景：当用户说'学习 PyTorch'、'pytorch 教学'、'开始上课'、'继续学习'、'复习'、'下一课'、'pytorch lesson' 等与 PyTorch 学习相关的请求时使用。
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.