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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audio-speak-voicebox
by yuisekiこの KDE Plasma / PipeWire 環境で VOICEBOX（VOICEVOX）を使ってオーナーに音声で話しかける。VOICEVOX API(50021) の確認、音声合成、HDMI sink への再生、volumeScale=2.5 の運用、ハマりポイント切り分けを行う。ユーザーが『VOICEBOXで話しかけて』『VOICEVOXで喋って』など依頼したときに使う。
audio-stt-whisper
by yuisekiwhisper.cpp（whisper-server）と PipeWire マイク入力を使って、日本語の音声待ち受け・文字起こし・音声コマンド待機（tmux常駐）を運用する。listen-only 実験、voice command loop（VOICEVOX応答 + VacuumTube操作）、モデル切替（small/medium）、マイク source 切替（DJI MIC MINI / Razer など）、ログ確認・デバッグを行う。ユーザーが『マイクで話しかけたい』『音声待ち受けを常駐したい』『whisper.cpp で音声認識したい』など依頼したときに使う。
system-reboot-bringup
by yuisekiUbuntu/KDE デスクトップを再起動したあとに、この環境の常駐プロセス群（VOICEVOX API、VacuumTube、whisper.cpp 音声コマンド待受、acaption/asec オーバーレイ、ASEE Viewer ウェブカメラオーバーレイ）を順序よく復旧する。ユーザーが『再起動後の起動手順をやって』『常駐プロセスを全部立ち上げて』『音声待受とカメラ監視を復旧して』など依頼したときに使う。
audio-play
by yuisekiKDE Plasma / PipeWire(PulseAudio互換) 環境で通知音・効果音・テストトーン・VOICEVOX発話（VOICEBOX表記含む）を確実に再生する。現在は repos/acaption の IPC を使った字幕付き再生/通知を主経路とし、paplay/notify-send をフォールバックとして扱う。
audio-speak-voicebox
by yuisekiこの KDE Plasma / PipeWire 環境で VOICEBOX（VOICEVOX）を使ってオーナーに音声で話しかける。VOICEVOX API(50021) でWAV合成し、repos/acaption の IPC 経由で音声+字幕を同期再生する。必要時のみ paplay 直再生へフォールバックする。
audio-stt-whisper
by yuisekiwhisper.cpp（whisper-server）と PipeWire マイク入力を使って、日本語の音声待ち受け・文字起こし・音声コマンド待機（tmux常駐）を運用する。listen-only 実験、voice command loop（VOICEVOX応答 + VacuumTube操作）、モデル切替（small/medium）、マイク source 切替（DJI MIC MINI / Razer など）、ログ確認・デバッグを行う。ユーザーが『マイクで話しかけたい』『音声待ち受けを常駐したい』『whisper.cpp で音声認識したい』など依頼したときに使う。
system-reboot-bringup
by yuisekiUbuntu/KDE デスクトップを再起動したあとに、この環境の常駐プロセス群（VOICEVOX API、VacuumTube、whisper.cpp 音声コマンド待受、acaption/asec オーバーレイ、ASEE Viewer ウェブカメラオーバーレイ）を順序よく復旧する。ユーザーが『再起動後の起動手順をやって』『常駐プロセスを全部立ち上げて』『音声待受とカメラ監視を復旧して』など依頼したときに使う。
audio-play
by yuisekiKDE Plasma / PipeWire(PulseAudio互換) 環境で通知音・効果音・テストトーン・VOICEVOX発話（VOICEBOX表記含む）を確実に再生する。現在は repos/acaption の IPC を使った字幕付き再生/通知を主経路とし、paplay/notify-send をフォールバックとして扱う。
audio-speak-voicebox
by yuisekiこの KDE Plasma / PipeWire 環境で VOICEBOX（VOICEVOX）を使ってオーナーに音声で話しかける。VOICEVOX API(50021) でWAV合成し、repos/acaption の IPC 経由で音声+字幕を同期再生する。必要時のみ paplay 直再生へフォールバックする。
audio-stt-whisper
by yuisekiwhisper.cpp（whisper-server）と PipeWire マイク入力を使って、日本語の音声待ち受け・文字起こし・音声コマンド待機（tmux常駐）を運用する。listen-only 実験、voice command loop（VOICEVOX応答 + VacuumTube操作）、モデル切替（small/medium）、マイク source 切替（DJI MIC MINI / Razer など）、ログ確認・デバッグを行う。ユーザーが『マイクで話しかけたい』『音声待ち受けを常駐したい』『whisper.cpp で音声認識したい』など依頼したときに使う。
owner-attention-call
by yuiseki手動確認や意思決定が必要になったときに、ASEE の owner presence を見て呼びかけ経路を切り替える。owner が見えていれば asay/VOICEVOX + acaption で『ユイさま、…』と話しかけ、見えていなければ ntfy で通知する。
system-reboot-bringup
by yuisekiUbuntu/KDE デスクトップを再起動したあとに、この環境の常駐プロセス群（VOICEVOX API、VacuumTube、whisper.cpp 音声コマンド待受、acaption/asec オーバーレイ、ASEE Viewer ウェブカメラオーバーレイ）を順序よく復旧する。ユーザーが『再起動後の起動手順をやって』『常駐プロセスを全部立ち上げて』『音声待受とカメラ監視を復旧して』など依頼したときに使う。
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.