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...
idea-from-daily
by LiYu0524Bridge daily recommender ideas to research pipelines. Read history/ideas/{date}/ideas.json OR generate ideas yourself from today's scored items, then route to /idea-creator, /idea-discovery, or /research-pipeline. Use when user says '/idea-from-daily', '从今日推荐启动研究', 'pick idea from daily'.
ideer-daily-paper-chatbot
by LiYu0524Use iDeer as a daily paper-reading workflow for chatbot-first users such as Codex, Gemini, or ChatGPT. Keep the original iDeer paper-digest setup, source selection, history validation, email/report/ideas workflow, but replace in-repo LLM API summarization and scoring with the current chatbot session. 适用于不用单独配置 OpenAI/SiliconFlow/Ollama API key 的每日论文整理、报告、想法生成与自动化。
ideer-daily-paper
by LiYu0524Daily paper/repo digest where YOU are the reader. Fetch items from arXiv/HuggingFace/GitHub/Semantic Scholar, then read, score, summarize, and generate ideas yourself — no external LLM API calls. Use when user says '今日论文', 'daily paper', 'daily digest', '每日推荐', or wants a personalized research briefing.
paper-banana
by LiYu0524学术插图生成 - 使用 PaperBanana 多智能体框架从方法文本自动生成框架图和统计图
auto-research
by LiYu0524一站式学术研究工作流：论文检索与阅读(arXiv + Zotero)、文献综述写作(Google Docs)、 论文精读与审稿(paper-reviewer)、学术写作Prompt工具箱(academic-writing)、 学术插图生成(PaperBanana)、架构图绘制(draw.io)、演示文稿制作(python-pptx / Pencil)。 整合 paper-research、paper-reviewer、academic-writing、google-docs、paper-banana、drawio、zotero-mcp、pptx 八大子技能。
paper-reviewer
by LiYu0524Review research papers (especially PDFs). Use when the user asks to read/通读/讲解/总结/审稿 a paper and wants a Chinese-first explanation of what it does, what is novel (创新点), plus reviewer-style strengths/weaknesses, major/minor concerns, and questions to authors.
paper-research
by LiYu0524End-to-end paper research support for arXiv/literature surveys, reproducibility-focused paper shortlisting, and experiment design. Use when you need to (1) search arXiv with complex queries, (2) download PDFs, extract text/sections, and fetch BibTeX, (3) dedupe/cluster results into a structured report, and (4) turn findings into a lit-review plan, benchmark/evaluation suite, and representation/probing experiment checklist (e.g., implicit reasoning, hidden-CoT, multilingual reasoning, cross-lingual alignment).
academic-writing
by LiYu0524学术论文写作 Prompt 工具箱：中英翻译、润色、缩写/扩写、逻辑检查、去 AI 味、 实验分析、图表标题生成、架构图描述、实验绘图推荐、Reviewer 视角审稿。 来源：awesome-ai-research-writing（MSRA / Seed / SH AI Lab 等顶尖研究机构实战 Prompt）。
google-docs
by LiYu0524Manage Google Docs and Google Drive with full document operations and file management. Includes Markdown support for creating formatted documents with headings, bold, italic, lists, tables, and checkboxes. Also supports Drive operations (upload, download, share, search).
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.