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...
paper-interview
by hyeshikGenerate a podcast-style in-depth scientific interview that introduces an academic paper. Uses multi-agent analysis (field expert, methods specialist, context historian, critical reviewer, accessibility translator, impact assessor) to prepare rich source material, then an editor agent curates the narrative, and a writer agent composes the final interview between a professional science interviewer and the paper's author. The user drops a PDF of the paper; all supplementary context is gathered via web search and PubMed/bioRxiv. MANDATORY TRIGGERS: "paper interview", "podcast interview for paper", "introduce this paper", "generate interview for this paper", "paper podcast", "deep dive interview", "interview about this paper", "논문 인터뷰", "논문 소개 인터뷰", "paper introduction interview". Also trigger when the user uploads a PDF and asks for a podcast, interview, deep dive, or accessible introduction of a scientific paper.
manuscript-review-panel
by hyeshikRun a multi-perspective review panel on a manuscript draft to help authors improve it for high-impact publication. Simulates 13 specialist reviewers who each review the paper, discuss disagreements, and produce a synthesized improvement roadmap. Agents search PubMed, bioRxiv, and the web for references. MANDATORY TRIGGERS: manuscript review, paper review, review my paper, review this manuscript, review panel, improve my paper, pre-submission review, mock peer review, reviewer feedback, review draft, multi-agent review, help me improve this paper, feedback on my manuscript, strengthen my paper. Use when the user uploads a manuscript (PDF, DOCX, or text) and asks for review, feedback, or improvement suggestions — even casually like "what do you think of this paper" or "can you review this draft". Also trigger for anticipating reviewer comments or pre-submission checks.
snu-procurement-doc
by hyeshik서울대학교 고가 연구장비 구매 규격서(구매규격서)와 용도설명서를 자동 생성하는 스킬. 사용자가 제조사, 모델명, 수량을 입력하면 웹 검색을 통해 관세분류번호(HSK), 정부물품분류번호, 장비 사양을 조사한 뒤, HWPX 양식에 맞는 두 문서를 생성한다. MANDATORY TRIGGERS: 구매규격서, 용도설명서, procurement, 장비구매, equipment purchase, 규격서, specification, 연구장비, lab equipment, HWPX 양식 작성, HWP 양식 작성. 이 스킬은 300만원 이상의 분자생물학/세포생물학/전산생물학 연구장비 구매에 사용한다.
paper-readers-guide
by hyeshikGenerate a treasure-hunt style reading guide for an academic paper — a typeset PDF of 20 quest questions that pulls the reader through the paper. Use when the user has a paper PDF and wants a reading guide, study guide, reading quest, questions to work through while reading, or "help me actually read this paper" — English or Korean output.
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.