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.
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mcp-analyze-topic
by AtmosphereExpert analyst persona used by the MCP analyze-topic tool to produce structured topic analyses. Use when invoked through the Atmosphere MCP server's analyze-topic tool.
scientific-podcast-summary
by aipochAutomatically summarize scientific podcasts like Huberman Lab and Nature.
expert-interview-generator
by aipochGenerates a full expert interview article including introduction, Q&A body, and summary based on interview questions and expert background. Use when you have interview questions and an expert profile and need a polished article.
twmd-news-lens
by frank890417Weekly news-lens 三源交叉 + news-driven spore candidate propose via EVOLVE-PIPELINE v2.0. Routine fires Sunday 01:00. Manual /twmd-news-lens or "跑 news lens" or "新聞透鏡掃描". TRIGGER when: routine twmd-news-lens-weekly fires / user says "跑 news lens" / "新聞透鏡掃描" / "三源交叉找熱點".
documentary-research
by revfactoryA full production pipeline where an agent team collaborates to generate documentary research, treatments, interview questions, and narration scripts all at once. Use this skill for 'plan a documentary,' 'create a documentary treatment,' 'documentary research,' 'create interview questions,' 'write a narration script,' 'investigative reporting plan,' 'documentary scenario,' and all other aspects of documentary production. Also supports treatment writing or narration scripting when existing research materials are provided. Note: actual video filming/editing (Premiere, DaVinci), interview scheduling/conducting, and broadcast transmission are outside the scope of this skill.
interview-design
by revfactoryA specialized skill for the interviewer agent covering documentary interview design. Provides interviewee selection, question sequence design, emotional elicitation techniques, and ethical interview principles. Use for 'interview questions,' 'interview subjects,' 'testimony collection,' 'interview design,' and similar topics.
investigative-research
by revfactoryA specialized skill for the researcher and fact-checker agents covering investigative research. Provides primary/secondary source collection, source reliability assessment, data triangulation, and bias analysis methodologies. Use for 'research,' 'fact-checking,' 'source verification,' 'investigative techniques,' and similar topics.
interview-design
by revfactory인터뷰어(interviewer)가 사용하는 다큐멘터리 인터뷰 설계 전문 스킬. 인터뷰 대상 선정, 질문 시퀀스 설계, 감정 유도 기법, 윤리적 인터뷰 원칙을 제공한다. '인터뷰 질문', '인터뷰 대상', '증언 수집', '인터뷰 설계' 등에 활용한다.
investigative-research
by revfactory리서처(researcher)와 팩트체커(fact-checker)가 사용하는 탐사 리서치 전문 스킬. 1차/2차 자료 수집, 출처 신뢰도 평가, 데이터 삼각검증, 편향성 분석 방법론을 제공한다. '자료 조사', '팩트체크', '출처 검증', '탐사 기법' 등에 활용한다.
morning-intelligence
by mohitagw15856Interviews you across 15 questions to capture your role, topics, sources, exclusions, and format preferences, then writes a master prompt you can paste into a scheduled task or Claude Code Routine. Use when you want to set up a personalised daily news brief, build a reusable morning news prompt, or create an automated intelligence briefing. Produces a confirmed summary of your preferences, a ready-to-paste master prompt, and setup instructions for both Cowork Scheduled Tasks and Claude Code Routines.
bilibili-search
by whiteguo233Search for videos on Bilibili using keyword queries generated from user interests.
bbc-news
by sundial-orgFetch and display BBC News stories from various sections and regions via RSS feeds. Use when the user asks for BBC news, UK news headlines, world news from BBC, or news from specific BBC sections (technology, business, politics, science, health, entertainment, regional UK news, or world regions).
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.