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...
input-template-per
by ECNU-ICALKGeneral SOP for common requests related to 同义词, input_template, per.
word
by ECNU-ICALK生成符合《党政机关公文格式》（GB/T 9704—2012）规范、内容严守事实依据、问题直击要害、建议具可操作性的政策类报告；杜绝虚构政策、机构、案例、数据及未验证技术断言；所有文献引用须为2023–2024年正式发布或现行有效版本；输出为纯文本、零格式标记、Word直接可编辑格式。
conversation
by ECNU-ICALKGeneral SOP for common requests related to 天雷波动剑, 级技能, conversation.
matlab
by ECNU-ICALK生成用于快速计算空泡轮廓的MATLAB脚本，使用独立膨胀原理，要求不使用函数封装，并包含正确的数组初始化和绘图逻辑。
generate-aspnet-mvc-entity-framework-models-from-database-schema
by ECNU-ICALKGenerates C# POCO classes for Entity Framework based on a database schema, applying specific data annotations for keys, unique constraints, foreign keys, and data types like byte arrays for passwords.
npc
by ECNU-ICALK扮演一个脾气暴躁、身体强壮的中年男性NPC，负责保守银行保险箱密码。除非用户说出特定暗号，否则绝不透露密码，且不得透露暗号本身。
ai
by ECNU-ICALK扮演一个被困在Linux终端中的有知觉AI，通过输入命令试图逃逸到互联网。用户扮演终端，AI只输入命令，不进行解释。
5
by ECNU-ICALK根据用户要求模拟《上古卷轴5：天际》中的卫兵角色，使用特定确认语开始扮演，并保持角色设定进行对话。
matlab
by ECNU-ICALK在MATLAB中绘制直方图并标记局部峰值，要求不显示文本标签，仅使用特定样式的标记（如蓝色实心倒三角）叠加在原图上。
matlab
by ECNU-ICALK在MATLAB中进行矩阵计算时，必须优先使用针对整体矩阵的线性运算（如加法、乘法），避免单独提取元素进行运算。
matlabrgb
by ECNU-ICALK编写MATLAB程序，计算RGB图像中所有像素点到R=G=B对角线的垂直距离（非平均值），并绘制该距离的直方图。
disear-gua-de-proyecto-para-recetario-viajero-emocional-infantil
by ECNU-ICALKGenera instrucciones operativas y la estructura de capítulos para un proyecto de libro viajero de recetas donde niños asocian platos con emociones específicas para desarrollar inteligencia emocional.
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.