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...
run-learning-retrospective
by cursorEvaluate learning progress, identify blockers, and adjust the learning plan
bootcamp-guide
by zts212653CVO 新手训练营引导模式。 Use when: thread 有 bootcampState（系统自动注入，不需要手动加载）。 Not for: 非训练营线程、老用户。
spaced-repetition
by revfactory간격 반복(Spaced Repetition) 알고리즘을 활용한 어휘·문법 복습 스케줄 설계 전문 스킬. review-coach 에이전트가 에빙하우스 망각곡선에 기반한 최적 복습 주기를 산출하고 장기 기억 전환율을 극대화할 때 활용한다. '간격 반복', '에빙하우스', '복습 스케줄', '망각곡선', 'SRS', 'Anki 방식' 등의 맥락에서 자동 적용한다. 단, Anki/Quizlet 등 외부 앱 연동이나 실시간 알림 시스템 구축은 이 스킬의 범위가 아니다.
ai-review-skill
by NeuroDongGenerates structured AI paper reviews (SoT style) for LaTeX, PDF, and Word manuscripts. Uses SoT prompt in English for English papers and SoT prompt in Chinese for Chinese papers. Use when the user asks to review a paper, 审稿, 论文审稿, review manuscript, or get strengths/weaknesses/suggestions for a .tex, .pdf, or .docx file.
ai-tutor
by spring-ai-communityUse when user asks to explain, break down, or help understand technical concepts (AI, ML, or other technical topics). Makes complex ideas accessible through plain English and narrative structure. Use the provided scripts to transcribe videos
study
by alaliqingUse this skill when the user wants to read, study, analyze, or deeply understand a research paper (PDF).
student-success-scorecard
by MicrockMetrics framework for monitoring engagement, progression, and completion.
academic-study-methods
by wentoraiEvidence-based study techniques for academic learning and retention
glossary
by whawkinsivUse this skill when the user doesn't understand a technical term, asks 'what is a [technical concept],' 'what does [term] mean,' seems confused by jargon in another skill, or is encountering developer concepts for the first time. Covers the 50+ technical terms non-technical founders encounter most, explained in plain English with no jargon.
educateme
by membranedevEducateMe integration. Manage Courses, Users, Enrollments, Categories, Reviews. Use when the user wants to interact with EducateMe data.
stage1-neurips-breakdown
by runtsangBreak down full NeurIPS reviewer responses into structured rebuttal units. Use when input contains NeurIPS reviewer fields (Summary, Strengths and Weaknesses, Questions, Limitations) and numeric scores (Rating, Confidence, Quality, Clarity, Significance, Originality). Splits questions and limitations into granular response items while preserving original wording for quoted issues.
stage2-iclr-refine
by runtsangRefine a Stage2 ICLR rebuttal draft into polished, reviewer-facing prose in the author's style; preserve factual grounding, optionally prepend a courteous opening phrase, and normalize tables/code/formulas into Markdown.
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.