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...
kaoyan-english-quiz
by TreasoniThis skill handles vocabulary quizzes and testing for 考研英语 (Chinese graduate entrance English exam). Use it when users want to test vocabulary knowledge with meaning quizzes, collocation exercises, polysemy-focused tests, or track quiz results with detailed error analysis.
kaoyan-english
by TreasoniThis skill routes English vocabulary learning requests to specialized sub-skills for 考研英语 (Chinese graduate entrance English exam) preparation. It handles vocabulary organization from PDF exports, spaced repetition schedules, quizzes, polysemy (rare word meanings) detection, word lookup, and writing output practice with MemOS integration for persistent tracking.
kaoyan-info
by TreasoniThis skill should be used when the user asks to collect graduate entrance exam (考研) information, create/modify exam preparation documents, or update existing study guides with latest data.
kaoyan-math
by TreasoniThis skill routes mathematics learning requests to specialized sub-skills for 考研数学 (Chinese graduate entrance math exam) preparation, including note generation with LaTeX formatting, knowledge point structure templates, and core infrastructure with MemOS integration for persistent mistake tracking and cross-device synchronization.
kaoyan-plan
by TreasoniThis skill should be used when the user asks to generate study plans for 考研 (Chinese graduate entrance exam), parse course schedules, create daily/weekly study schedules, or optimize study time allocation. Supports three input modes (minimal/standard/advanced), adapts to individual chronotypes (morning person/night owl), handles task debt from missed plans with circuit breaker protection (>10h triggers recovery mode), enforces Sunday review, respects minimum block duration requirements for different subjects, implements science-based time block splitting based on cognitive science (attention decay, decision fatigue, spacing effect), integrates with MemOS for persistent learning progress tracking, includes context refresh mechanism (auto-prompts profile update after 30 days), mental health intervention (triggers after 3 consecutive tired days), vocabulary review validation (prevents missing newly learned vocabulary), plan upsert logic with tagging for version control, completion record generation based on user
mistake-book
by TreasoniThis skill helps users quickly organize mistakes/errors into subject-specific mistake notebooks. It supports multiple subjects (math, electronics, English), auto-formats mistake entries following the established template with LaTeX support, auto-updates the index table, and appends to existing mistake notebooks without overwriting. Use when user says "整理错题", "记错题", "错题笔记", "把这道题记到错题本", or provides mistake content for recording.
kaoyan-electronics-circuit
by Treasoni电路图解析 - 管理822电子技术基础的电路图智能识别、元件参数提取、电路拓扑分析、静态分析+动态分析输出，使用MCP工具实现电路结构识别，强制康华光符号体系
kaoyan-electronics-structure
by Treasoni知识点结构 - 管理822电子技术基础的知识点图谱（模电+数电）、前置知识关联、跨章节提示、知识点卡片模板、常见错误模式，复用knowledge_graph_electronics.yaml和knowledge_card_electronics.md
kaoyan-electronics-sop
by TreasoniSOP模板库 - 管理822电子技术基础的17个标准化解题流程（模电8个+数电9个），包含解题步骤引导、答题检查清单、康华光符号体系强制、LaTeX电子符号标准、Mermaid波形图生成
kaoyan-math-summary
by Treasoni整理考研数学章节笔记，生成结构化总结文件。**触发词**："章节总结"、"整理章节"、"汇总这一章"、"章节笔记"、"数学笔记总结"、"整理数学笔记"、"做一个总结"、"总结一下这一章"、"帮我总结"。功能包括：提取重要定义定理公式、保留个人理解、按章节结构组织、生成📝章节总结.md文件。
kaoyan-english-vocab
by TreasoniThis skill handles vocabulary organization and word lookup for 考研英语 (Chinese graduate entrance English exam). Use it when users want to extract vocabulary from PDF exports (墨墨/百词斩), generate real exam context articles, detect polysemy (rare word meanings), look up word information, or organize vocabulary cards.
kaoyan-electronics
by TreasoniThis skill routes 822 electronics learning requests to specialized sub-skills for 湖南大学822电子技术基础考研 preparation, including circuit diagram analysis, SOP templates for 17 problem types, knowledge point structure, and MemOS integration for persistent tracking.
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.