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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research-assistant
by revfactoryA pipeline where an agent team systematically performs academic research assistance. Use this skill for requests such as 'search for papers,' 'literature review,' 'organize research materials,' 'academic research,' 'organize references,' 'prior research analysis,' 'research trend analysis,' 'literature search,' or 'academic note organization.' Note: experiment execution, statistical analysis execution, final paper writing, and journal submission are outside the scope of this skill.
class-learn
by XiaoLuoLYGAttend class or study using the school and library workflow.
memstack-development-mentor
by cwinvestmentsUse when the user says 'teach me', 'explain as you go', 'mentor mode', 'walk me through', 'help me learn', 'explain why', 'learning mode', or wants real-time plain language narration of decisions and tradeoffs while building. Do NOT use for code review or debugging.
thesis-docx
by the-shy123456Create, revise, and format thesis or dissertation Word documents with strict academic formatting control. Use when an AI agent needs to generate or revise thesis content, normalize Word styles, follow a school template, fix captions or page numbers or section levels, or produce evidence-based Mermaid figures and LaTeX-formatted code listings for a thesis document.
research-assistant
by modu-ai학술 리서치와 논문 작성을 지원합니다. '리서치 보고서 작성해줘', '논문 구조 잡아줘', '문헌 검토 정리해줘'라고 요청할 때 사용하세요. 데이터 수집·분석, 학술 논문 초안 작성, 연구 설계, 인용·참고문헌 관리, 피어 리뷰 대비를 지원합니다.
thesis-defense-pptx
by zouchenzhenCreate, polish, and quality-check editable undergraduate or graduate thesis defense PowerPoint decks from a local thesis PDF/LaTeX project and an existing PPTX template. Use when the user asks for a formal defense PPT, thesis presentation, academic答辩PPT, template-matched PPTX, or local PowerPoint deck generation with visual inspection.
teaching-assistant
by ntygod当用户要学习概念、让你解释原理、生成练习题、规划学习路径、通过代码演示理解知识点时使用。
nsfc-reviewers
by InternScience当用户明确要求"评审国自然标书"、"模拟专家评审"、"审阅 NSFC 申请书"时使用。模拟领域专家视角对 NSFC 标书进行多维度评审，输出分级问题与可执行修改建议。⚠️ 不适用：用户只是想写/改标书某个章节（应使用 nsfc-*-writer 系列技能）、只是想了解评审标准（应直接回答）、没有明确"评审/审阅"意图。
class-note-builder
by wpsnote当用户希望把课堂逐字稿、OCR 笔记、截图资料或零散学习内容整理成结构化的 WPS 学习笔记时使用此 Skill。适合课堂记录整理、培训复盘、补课笔记汇总、课程主笔记构建等场景，尤其适合原始材料很乱、但最终需要一篇可复习、可回看、可继续补充的主笔记时。
article-render-pdf
by hqhq1025Use when the user provides a URL to a web article, blog post, or X Article and wants LaTeX notes rendered as PDF. Triggers: article/blog URL, '整理这篇文章', '文章笔记'. Handles translation detection (traces back to original).
bilibili-render-pdf
by hqhq1025Use when the user provides a Bilibili URL (BV number) and wants LaTeX course notes rendered as PDF. Triggers: Bilibili link, BV号, 'B站视频笔记', '整理B站课'. Falls back to Whisper when no CC subtitles.
master-thesis-studio
by darksider-9Use this skill for Southeast University-style master's thesis projects from a Word template or existing draft, including optional reverse parsing of an existing DOCX, user intake, outline planning, chapter drafting, academic asset management, Markdown visualization, references, figures, tables, formulas, and safe DOCX generation through the bundled Word/XML scripts.
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.