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...
characteristic-voice
by NoizAIUse this skill whenever the user wants speech to sound more human, companion-like, or emotionally expressive. Triggers include: any mention of 'say like', 'talk like', 'speak like', 'companion voice', 'comfort me', 'cheer me up', 'sound more human', 'good night voice', 'good morning voice', or requests to add fillers, emotion, or personality to generated speech. Also use when the user wants to mimic a specific character's voice, apply speaking style presets (goodnight, morning, comfort, celebration, chatting), tune emotional parameters like warmth or tenderness, or make TTS output feel like a real person talking. If the user asks for a 'voice message', 'companion audio', 'character voice', or wants speech that sighs, laughs, hesitates, or sounds genuinely warm, use this skill. Do NOT use for plain text-to-speech without personality, music generation, sound effects, or general coding tasks unrelated to expressive speech.
25-voice-clone-podcast-global
by minhnv0807Audio AI for global personal brand — voice clone (ElevenLabs, Murf, PlayHT), podcast, audiobook, voiceover. 3 use cases: short voiceover (TikTok/Reels), podcast 30-60min, audiobook. 1:10 repurpose (1 podcast → 10 short clips). English (US/UK/AU/SG accents available). Trigger: 'voice clone global', 'ElevenLabs', 'podcast AI', 'audiobook AI', 'voiceover AI'.
performing-orthonotone-polychoral-instrument
by aiskillstoreGuides agents through launching, playing, sculpting, and capturing performances with the Orthonotone polychoral instrument MVP. Use when generating music, soundscapes, or live demos from this repository.
ableton-songwriter
by uisatoProfessional songwriting workflow for Ableton: structured intake, production brief, composition/arrangement execution, plugin-aware instrument loading, quick mix, QA, and revision handoff.
song-lyrics
by NeverSightProvides insights and techniques from renowned songwriters across different genres to help users improve their own lyrical compositions.
sermon-planner-52week
by idoforgod키워드 1개를 입력받아 52주 연간 설교 계획을 자동 생성하는 설교 기획 도우미. 각 주차별로 설교 주제, 핵심 성경구절 5개, 핵심 포인트 3개, 세부 주제 5개, 연관 찬송가 5곡을 제시하며, 절기(부활절/성탄절/맥추감사절 등)와 한국교회 트렌드(고립·세대갈등·회복·환경 등)를 자동 반영한다. 사용자가 "52주 설교 계획", "연간 설교 주제", "설교 시리즈 기획", "키워드로 설교 주제", "주일설교 연간 계획", "설교 아이디어", "사경회/특새 주제", "절기 설교"를 언급하거나, 단일 신앙 키워드(소망/감사/믿음/사랑/제자도/은혜/회개/순종 등)로 설교 기획을 요청할 때 반드시 발동한다. 목회자·부교역자·신학생·소그룹 리더의 설교 준비 시간 단축과 신학적 균형을 목적으로 한다.
performing-orthonotone-polychoral-instrument
by ComeOnOliverGuides agents through launching, playing, sculpting, and capturing performances with the Orthonotone polychoral instrument MVP. Use when generating music, soundscapes, or live demos from this repository.
album-conceptualizer
by diegosouzapwAlbum concepts, tracklist architecture, and thematic planning
lyriccraft
by diegosouzapwCollaborative lyric writing with section-by-section approval
performing-orthonotone-polychoral-instrument
by diegosouzapwGuides agents through launching, playing, sculpting, and capturing performances with the Orthonotone polychoral instrument MVP. Use when generating music, soundscapes, or live demos from this repository.
beat
by diegosouzapw16ステップビートを生成 (JSON + ASCII grid + MIDI + WAV)。スタイルプロンプトからビートを作成。トリガー: /beat, ビートを生成, ビートを作って
pronunciation-specialist
by diegosouzapwScan lyrics for pronunciation risks, prevent Suno mispronunciations
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.