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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podcast-studio
by revfactoryA full production pipeline where an agent team collaborates to generate podcast episode content — planning, research, scripting, show notes, and distribution strategy — all at once. Use this skill for requests like 'plan a podcast episode,' 'write an episode script,' 'podcast scriptwriting,' 'podcast show notes,' 'episode planning,' 'interview script,' 'podcast distribution,' 'audio content planning,' and other podcast production tasks. Also supports show note or distribution package creation when an existing script or research is provided. Note: actual audio recording/editing (Audacity, GarageBand), podcast hosting API integration, and RSS feed technical setup are outside this skill's scope.
podcast-studio
by revfactory팟캐스트 에피소드의 기획, 리서치, 대본, 쇼노트, 배포 전략을 에이전트 팀이 협업하여 한 번에 생성하는 풀 프로덕션 파이프라인. '팟캐스트 기획해줘', '에피소드 대본 써줘', '팟캐스트 스크립트', '팟캐스트 쇼노트 만들어줘', '에피소드 기획', '인터뷰 대본', '팟캐스트 배포', '오디오 콘텐츠 기획' 등 팟캐스트 제작 전반에 이 스킬을 사용한다. 기존 대본이나 리서치가 있는 경우에도 쇼노트나 배포 패키지 제작을 지원한다. 단, 실제 오디오 녹음/편집(Audacity, GarageBand), 팟캐스트 호스팅 API 연동, RSS 피드 기술적 설정은 이 스킬의 범위가 아니다.
sag
by understudy-aiElevenLabs text-to-speech with mac-style say UX.
morning-podcast
by THU-SAGEGenerate a short spoken morning news briefing as an audio clip.
play-by-play-generator
by OneWave-AICreate realistic play-by-play commentary. Multiple announcer styles: traditional, hyped, analytical, homer. Color commentary included.
sports-podcast-outline-generator
by OneWave-AICreate structured podcast episodes. Segment timing, debate points, hot takes, listener questions, ad break placement.
streaming-recommendations
by NeverSightRecommends movies and TV shows to watch on streaming services. Gives 3-5 curated picks based on genre, mood, format, and streaming platform. Use when the user asks what to watch, wants streaming recommendations, or needs help picking a movie or show.
podcast-interview
by guia-matthieuMaster the art of podcast interviewing using NPR training methodology and Tim Ferriss's preparation techniques to extract compelling stories and insights from any guest. Use when: Preparing for a podcast interview with a guest; Designing questions that elicit stories, not soundbites; Struggling to get guests to open up authentically; Planning a new interview-format podcast; Improving your interviewing technique
podcast-production
by ArgentAIOSPlan, generate, mix, and publish a daily multi-persona podcast episode using podcast_plan + podcast_generate, with optional HeyGen video and YouTube upload flow.
podcast
by marswaveaiCreate podcasts from topics, URLs, or text. Triggers on: "做播客", "podcast", "播客", "录一期节目", "chat about", "discuss", "debate", "dialogue", "make a podcast about".
anchor
by diegosouzapwCreate and distribute podcasts with Anchor (Spotify for Podcasters) - manage episodes, analytics, and distribution
newsroom
by diegosouzapwAI Newsroom: Generate professional podcasts, debates, and news briefings from any topic using ElevenLabs.
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.