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...
lecture-to-notes
by ysyecustGenerate professional, information-dense, figure-rich LaTeX course notes and compiled PDF from a YouTube or Bilibili lecture video. Use when the user provides a video URL and wants structured Chinese teaching notes. Key features include smart slide-region cropping (removes lecturer, keeps only slide content), three-level subtitle fallback (CC → Whisper → visual-only), dense frame sampling with contact-sheet review, and high information density writing. Trigger words include lecture notes, 课程笔记, 视频转PDF, 讲义, YouTube笔记, B站笔记, BV号.
paper-to-html
by ysyecustGenerate a self-contained, beautifully styled HTML analysis of an academic paper. Use when the user provides an arXiv link, paper URL, PDF, or asks to analyze a research paper. Produces a standalone HTML file with structured sections (Problem, Translation/Analogy, Architecture, Key Results, Verdict), embedded figures, and responsive design. Trigger words include 读论文, 分析论文, paper analysis, paper review.
numerical-patterns
by ysyecustNumerical computing patterns for C++20 including matrix operations, iterative solvers, numerical stability, data pipelines, and HPC I/O with MPI-IO and HDF5.
hpc-patterns
by ysyecustHigh-performance computing patterns for C++20 including cache-friendly data structures, SIMD vectorization, memory management, thread parallelism, lock-free data structures, and NUMA-aware allocation.
iterative-retrieval
by ysyecustPattern for progressively refining context retrieval to solve the subagent context problem
videodb
by ysyecustSee, Understand, Act on video and audio. See- ingest from local files, URLs, RTSP/live feeds, or live record desktop; return realtime context and playable stream links. Understand- extract frames, build visual/semantic/temporal indexes, and search moments with timestamps and auto-clips. Act- transcode and normalize (codec, fps, resolution, aspect ratio), perform timeline edits (subtitles, text/image overlays, branding, audio overlays, dubbing, translation), generate media assets (image, audio, video), and create real time alerts for events from live streams or desktop capture.
coding-standards
by ysyecustUniversal coding standards, best practices, and patterns for TypeScript, JavaScript, React, and Node.js development.
security-review
by ysyecustKimlik doğrulama eklerken, kullanıcı girdisi işlerken, secret'larla çalışırken, API endpoint'leri oluştururken veya ödeme/hassas özellikler uygularken bu skill'i kullanın. Kapsamlı güvenlik kontrol listesi ve kalıplar sağlar.
tdd-workflow
by ysyecustYeni özellikler yazarken, hata düzeltirken veya kod refactor ederken bu skill'i kullanın. Unit, integration ve E2E testlerini içeren %80+ kapsam ile test güdümlü geliştirmeyi zorlar.
android-clean-architecture
by ysyecust适用于Android和Kotlin多平台项目的Clean Architecture模式——模块结构、依赖规则、用例、仓库以及数据层模式。
autonomous-loops
by ysyecust自主Claude代码循环的模式与架构——从简单的顺序管道到基于RFC的多智能体有向无环图系统。
coding-standards
by ysyecust适用于TypeScript、JavaScript、React和Node.js开发的通用编码标准、最佳实践和模式。
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.