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...
thumbnail
by daiki-beppuUse when コレクションのサムネイル画像が必要で、CTR最適化されたプロンプト生成 + 画像生成プロバイダー（Gemini / OpenAI / codex）での画像生成を行いたいとき。サムネイル、画像生成、CTR改善、ビジュアル制作、アイキャッチ、main.pngなど、視覚コンテンツの作成に関わる場面で必ず使用すること。Do not use when: SVG・ベクター画像の生成/編集、コード生成、YouTube サムネイル以外の汎用画像生成（これらは本スキルの対象外）
thumbnail-compare
by daiki-beppuUse when サムネイルをベンチマーク競合と並べて比較検証したいとき。「サムネ比較」「サムネイル検証」「目立ってるか確認」「サムネ並べて」「モバイル表示テスト」「320px」など。文字サイズ・コントラスト・縮小表示での視認性を検証。方向性見直し時に必ず使用すること
automation-release
by daiki-beppuUse when youtube-automation リポジトリ本体の新規リリースを作成したいとき。`/automation-release` 1 コマンドで状態判定し、prepare（リリース PR 作成）または publish（tag + GitHub Release）に自動分岐する。「リリースして」「リリース作って」「新しいバージョン作って」「v5.6.0 出して」「/automation-release」で発動。グローバル `/release` は Node.js 向けで本リポジトリでは使わない。
automation-update
by daiki-beppuUse when 下流チャンネルリポジトリで youtube-channels-automation を upstream 最新リリースに追従させたいとき。「追従」「アップグレード」「最新版に上げて」「v5.x.y に上げて」「automation-update」「automation を更新」「skills sync 含めて更新」など、`pyproject.toml` の pin bump → `uv lock` → `yt-skills sync` → 動作確認 → コミットまでを 1 コマンドで回したい場面で使用する。GitHub Release 本文と `CHANGELOG.md` から累積影響を要約し、local fix 衝突や破壊的操作の前で人間に確認を求める AI 主導 wizard。
lyria
by daiki-beppuUse when Vertex AI Lyria 3 でマスター音源を自動生成したいとき。skill-config と Lyria 3 `interactions` REST API を組み合わせ、コレクション尺に合わせて複数セグメント (1 リクエスト = 最大 ~184 秒のオーディオ、API は MP3 を返し保存時に PCM s16le WAV へ変換) を生成しクロスフェード結合してマスター音源を出力する（人手介入なし、/masterup 不要、次工程は /videoup）。Suno で人手生成するチャンネルでは /suno を使う
audience-persona
by daiki-beppuUse when ターゲット視聴者のペルソナを定義・見直したいとき。「誰が聴くか」「ペルソナ設定」「ターゲット」「視聴者像」「ターゲット層」「リスナー像」「TTP の人物像版」など。/viewer-voice の結果を前提とし、/viewing-scene の入力になる。チャンネル立ち上げ・方向性見直し時に必ず使用すること
channel-research
by daiki-beppuUse when /channel-new で収集したベンチマークデータを徹底分析したいとき。「競合分析」「ベンチマーク分析」「チャンネルリサーチ」「競合を調べて」「TTP 対象抽出」など、新チャンネル開設時の競合チャンネル分析に関わる場面で使用すること。/channel-new の後、/channel-direction の前に実行する
alignment-check
by daiki-beppuUse when 各コレクションの音楽ムード × サムネイル雰囲気 × タイトル訴求の整合性を監査したいとき。「整合性チェック」「一致してるか確認」「タイトル見直し」「サムネと音楽の一致」「タイトル改善」「CTR改善」など。CTR に最も影響するチェックポイント。方向性見直し時に必ず使用すること
channel-direction
by daiki-beppuUse when /channel-research の分析結果をもとに新チャンネルの方向性を決定したいとき。「方向性決めたい」「チャンネルの方針」「ポジショニング」「差別化」「ブレスト」「TTP 対象を決める」など、新チャンネルの戦略的方向性を対話で決定する場面で使用すること。/channel-research の後、/channel-setup の前に実行する
wf-status
by daiki-beppuUse when コレクション制作の進捗を読むだけで確認したいとき（実行はしない）。「どこまで進んだ？」「workflow-state 見せて」「制作中コレクション一覧」など、collections/planning/ 配下の現在地を一覧・詳細表示するときに使用する。チャンネル登録者数など YouTube 側の統計は /channel-status
wf-next
by daiki-beppuUse when 既存コレクション（collections/planning/ 配下）の次の工程を実行したいとき。「次どうする？」「次のステップやって」「続き進めて」など、制作中コレクションを一段進めるときに使用する。読むだけで進捗を見たい場合は /wf-status、新規コレクション開始は /wf-new
wf-new
by daiki-beppuUse when まだコレクションディレクトリが存在せず、新規コレクション制作を立ち上げたいとき。「新しいコレクション始めたい」「制作開始」「新規ワークフロー」など、企画選択からディレクトリ作成・素材準備までを行う初期化フェーズで使用する。既存コレクションの進行は /wf-next
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.