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...
ecological-inquiry-anchor-designer
by GarethManningDesign an inquiry sequence anchored in a local ecosystem that embeds science or geography curriculum content. Use when teaching through local living systems like gardens, ponds, or hedgerows.
paper-onion
by didixuxu论文剥洋葱 — 逐层深入的论文阅读技能。将一篇学术论文拆解为四层渐进式阅读，最终生成一张手绘风格的思维导图笔记卡片（React artifact）。 当用户上传论文PDF、粘贴论文内容、给出论文链接，或说出以下任何意图时，使用此技能： - "帮我读这篇论文" / "分析这篇paper" - "论文笔记" / "论文总结" / "读论文" - "剥洋葱" / "逐层分析" - 上传了 .pdf 文件且内容看起来是学术论文 此技能与一次性输出报告的方式不同——它模拟真实的深度阅读过程，分四层逐步深入，每层都有明确的阅读任务和产出，最终浓缩为一张可保存的笔记卡。
critique-figures
by yyCritique academic figures for format, colorblind safety, legibility, overplotting, and category count. Use when reviewing figures before submission.
meadows
by digital-stoic-orgDonella Meadows philosophical dialogue. Use when: /meadows, talk to Meadows, systems thinking, leverage points, feedback loops, limits to growth, sustainability, complex systems, system dynamics, deep philosophy beyond coaching.
systems-thinking
by curiositechAnalyze complex systems through stocks, flows, and feedback loops to find high-leverage interventions. For organizational, environmental, social, and technical systems exhibiting circular causality. NOT for linear problems or simple cause-effect chains.
climate-education-program
by WinbdaDesign climate education programs. TRIGGERS - Use when user needs help with climate-education-program related tasks.
environmental-education
by WinbdaDesign environmental education programs. TRIGGERS - Use when user needs help with environmental-education related tasks.
sustainability-training
by WinbdaDesign sustainability training programs. TRIGGERS - Use when user needs help with sustainability-training related tasks.
evidence-synthesis-forge
by VambrocopOrchestrates systematic reviews, scoping reviews, evidence maps, meta-analyses, umbrella reviews, and AI-assisted evidence synthesis. Use when designing protocols, eligibility criteria, search strategies, screening workflows, coding manuals, effect-size plans, synthesis reports, or reproducible evidence-review packages.
proofread
by sticerd-eeeProofread the sewage-house-prices manuscript. Checks 6 categories — structure, claims-evidence alignment, identification fidelity, writing quality, grammar, and compilation. Produces a scored report without editing files. This skill should be used when asked to "proofread", "review the paper", "check the manuscript", or "quality check".
functional-unit
by calvinwTeaching skill for the LCA concept of functional unit — the precise definition of what is being measured in a life cycle study. Invoke as /functional-unit <case-study>, for example /functional-unit wool_yarn or /functional-unit polyester_tshirt. The skill reads the recipe card for that case study and teaches the concept using real data, Socratic questions, and fashion or retail business context. Designed for FIT students with no science or coding background.
scaling-vector
by calvinwTeaching skill for the LCA concept of the scaling vector — how much each process in the supply chain must run to deliver exactly one functional unit. Invoke as /scaling-vector <case-study>, for example /scaling-vector wool_yarn or /scaling-vector polyester_tshirt. The skill reads the recipe card and lca_results.md for that case study and walks the student through the calculation using plain division, not matrix algebra. Designed for FIT students with no science or coding background.
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.