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...
thinking-debate
by 53ableディベート思考を用いた多角的な意思決定プロトコル。
code-reading
by 53ableコードリーディングを3層フレームワーク（取得・処理・管理）で効率的に実行する。 コードの理解、調査、学習を求められた時に使用。複雑なケースではサブエージェント起動を推奨。
docs-site-add-visual-doc
by 53ableAdds Spell UI visual documentation pages to docs-site from repos/ sources: optional clone into repos/, Context Engineering (JiT loading, structured extraction, self-refinement), scan-source.sh inventory, Think–Structure–Style and templates, writes docs/<name>.html, links index.html, opens draft PRs. Encodes requested reading tone in concrete wording (short steps, clear labels, respectful imperatives) instead of meta section titles or brochure taglines. Aligns index card titles with the page title and subject matter. Use when converting READMEs, SKILLs, or cloned repos under repos/ into docs-site pages. Do not use for non-docs-site projects, source outside repos/, standalone generic HTML outside this site, large in-place rewrites of existing pages (do focused edits instead), or pages that only name a vibe in headings without substantive content.
self-refine
by 53ableプラン実行前にMulti-Aspect評価でセルフリファインを行い、品質を担保する。 「実行して」「進めて」「go」と依頼された時、プラン実行の直前に使用。
css-layout
by 53ableCSSのレイアウト課題に対し、中央配置・レスポンシブグリッド・ページ骨格・サイズ制御のパターンを選定し適用する。Flexbox、Grid、clamp、aspect-ratio、コンテナクエリを扱う。中央配置、スティッキーフッター、オーバーレイ、カードグリッド、幅制約の実装時に使う。アニメーション、タイポグラフィ刻度、カラーテーマ、JavaScript主導のレイアウトには使わない。
git-commit-granularity
by 53ableGitコミットの粒度（サイズ・単位）ベストプラクティスに従い、アトミックで単一概念のコミットへ分割・整形するガイドを提供する。git add -pによるハンクステージング、セマンティックギャップの回避、クリーンな履歴の構築手順をカバーする。コミット作成時、既存コミットのリファクタリング前、コードレビューでのコミット単位の見直し時に使う。コンフリクト解消、PRマージ、リポジトリの初期セットアップには使わない。
jobs-theory-innovation
by 53ableジョブ理論（Jobs-to-be-Done）に基づいてビジネス課題を発見し、イノベーション機会を特定するエージェントワークフロー。コンテキストエンジニアリング原則に従い、構造化されたメモリと6つの分析レンズ、Chain-of-Thoughtプロセスで顧客の真のジョブを探索・言語化する。「ジョブ理論で分析して」「顧客の本当のニーズを探りたい」「イノベーションのヒントが欲しい」「なぜ顧客が離れるのか分析して」と依頼された時に使用。
meeting-to-video
by 53ableミーティングのトランスクリプト（プレーンテキスト）から Remotion ストーリー型ビデオプロジェクトを生成する。npx remotion preview でローカルプレビューできる状態まで自動構築する。Use when: meeting transcript, meeting summary video, meeting recap video, ミーティング動画, 議事録動画。
presentation-zen-garr-reynolds
by 53ableガー・レイノルズのPresentation Zen原則でプレゼンを計画、設計、批評、修正する。聞き手中心のメッセージ、抑制、簡潔さ、自然さ、視覚的スライド、slideument分離、発表時の存在感を扱う。トーク作成、スライド構成、スピーカーノート、視覚設計指針、チェックリスト作成時に使う。汎用グラフィックデザイン、網羅的な学術引用レビュー、法務・規制文書、ライブ発表を前提にしない単体文書には使わない。
reliable-llm-app-principles
by 53able12-Factor Agents を中心に、信頼性の高い LLM アプリケーションを設計、レビュー、改善する手順を提供する。プロンプト所有、コンテキスト制御、構造化出力、統一状態、pause/resume、人間承認、制御フロー、エラー圧縮、小さなエージェント、外部トリガー、stateless reducer を扱う。LLM アプリ、エージェント実行基盤、tool-calling ワークフロー、本番信頼性レビューで使用する。一般的なプロンプト作成、モデル比較、UI 文言編集、LLM を含まない通常のアプリ設計には使用しない。
tdd-from-design-docs
by 53ableDesign Docsから指定された範囲を、TDD（テスト駆動開発）で実装するワークフロー。 「TDDで実装して」「テストファーストで」と依頼された時に使用。 Design Docsがない場合はコードベース・ユーザー入力から要件を収集して代替する。 単純なケースはスキル内で完結、複雑なケースはtdd-specialistサブエージェントへ委譲。
thinking-abduction
by 53ableアブダクション（仮説形成）を用いた構造化された推論プロトコル。
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.