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-write-sci
by huangwb8根据 LaTeX 论文项目撰写、修订和润色 SCI 期刊论文，默认 AI 自主模式，也支持人机协作仅输出审查计划；提供作者风格化写作、数字事实核验、逻辑树多轮审查与 PDF/Word 渲染闭环。⚠️ 不适用：仅改格式/样式参数、纯参考文献管理、图片处理、非论文写作任务。
data-availability
by Boom5426Use when drafting, auditing, or revising Data Availability statements, repository plans, accession-number placement, source-data coverage, or restricted-data wording for journal submission or resubmission.
research-paper-writing
by AlexAI-MCPAcademic paper writing workflow — literature review, LaTeX setup, structure, citations, and conference submissions.
arxiv-reading
by tririverUse this skill when the user provides an arXiv ID (e.g., arXiv:2508.12856) and asks to read, analyze, or extract equations from the paper. Activates for literature review, equation extraction, paper understanding, and cross-referencing tasks in academic research contexts.
manuscript-scaffold
by youngeun1209Scaffold a LaTeX manuscript directory for a research project — copy the bundled skeleton (main.tex + sections/* + figures/ + references.bib + .gitignore + README), optionally apply a journal-specific documentclass from templates/journal-registry.json, optionally clone an Overleaf project and cache the Git credential helper (token never persisted to tracked files), commit on the default branch, and ask before pushing. Invoked by /start-research phase 6, but also standalone-callable when adding a manuscript dir to an existing project later.
chem-paper-search
by DennisWei9898Searches Semantic Scholar and the open web for chemistry / chemical engineering / materials science papers given a topic and keyword sets. Returns a triaged table with title, first author, year, citation count, PDF availability, and one-line summary. Used as a sub-skill by `paper-mentor` during Phase 2; can also be invoked directly. Triggers on phrases like "find papers on", "幫我找文獻", "search Semantic Scholar", "literature on X".
paper-mentor
by DennisWei9898A virtual research advisor that walks graduate students through the entire research-to-writing process — picking a topic, finding papers, drafting, and final grading. Uses Socratic questioning instead of writing for the student. Loads a department-specific advisor profile (v0.2 ships with 4 profiles: chieh-ting-lin for chemistry/materials, social-science-default for sociology/polisci/psych/econ, humanities-default for literature/history/philosophy, cs-default for computer science/ML/systems). Triggers on phrases like 我想找教授討論、I want to talk to a professor、help me write my paper、幫我審論文、score my paper、研究方向、論文評分.
lab-report
by PiaoxuemoliWrite experiment and lab reports from source materials (lab manuals, PPT, Word, PDF). Use this skill whenever the user needs to write a lab report, experiment report, course report, 实验报告, 课程报告, or any structured academic/technical writeup based on experiment procedures. Proactively suggest this skill when the user mentions "lab report," "experiment report," "实验报告," or describes running an experiment that needs documenting.
peer-review
by plurigridSystematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.
paper-lens
by nekoneko0831论文阅读助手：速览/精读/学习/展示四模式阅读 + 批量检索/批量下载。当用户提供论文PDF、要求分析/阅读论文、说'帮我读这篇论文'、'精读文档'、'全文关键点梳理'、'搜索/检索论文'、或粘贴多个arXiv链接时触发。
paper-review-lite
by scdenneyPre-submission audit: argument, numerics, refs, writing, figures, replication.
gcse-chemistry-tutor
by markpittGCSE Chemistry tutor and revision assistant for 15–16 year old students preparing for 2026 exams across AQA, Edexcel, OCR, and WJEC boards. Use when a student asks for help understanding chemistry topics, answering exam questions, revising for GCSEs, practising required practicals, or wants guidance on exam technique for GCSE Chemistry.
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.