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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ipa-security-guide
by classmethodipa-security-check をはじめとするセキュリティ診断ツールが出力したレポートを読み込み、各検出項目を優先順位付きの dev-debug 依頼リストに変換する。対象プロジェクトの言語・FWを問わず汎用的に使える。コードベースを直接読んでアーキテクチャ判断を行う。
ipa-security-check
by classmethodIPA「安全なウェブサイトの作り方 改訂第7版」「安全なSQLの呼び出し方」「ウェブ健康診断仕様」「セキュリティ実装チェックリスト」「安全なウェブサイトの運用管理に向けての20ヶ条」に基づき、ソースコードを静的に検査して脆弱性候補を検出する。発見した問題には IPA 原典の出典 (文書名・章・ページ・URL) を必ず付与する。
dev-verify
by classmethodThis skill should be used when the user asks to "dev-verify", "実装の検証", "完了チェック", "verify implementation", "全テスト実行", "run all tests", "dev-verify auth". Plan単位で全タスクの完了状態とテスト・ビルド・Lintの整合性を検証し、レポートを出力する。
dev-webtest-plan
by classmethodThis skill should be used when the user asks to "dev-webtest-plan", "Webテスト計画を生成", "テスト計画を作成", "webtest plan", "E2Eテスト計画", "画面テスト計画", "generate webtest plan", "create test plan from requirements", "webtest計画を更新", "テスト計画の差分更新", "update webtest plan", "画面仕様からテスト更新". dev-planの出力からPlaywright用のWebテスト計画ファイルを自動生成する。画面仕様の変更差分からテスト計画を更新する差分更新モードも対応。
dev-webtest
by classmethodThis skill should be used when the user asks to "dev-webtest", "Webテスト", "画面の動作確認", "E2Eテスト", "web test", "visual check", "モンキーテスト", "アクセシビリティチェック", "レスポンシブテスト", "フォームテスト". Playwright CLIを使ってWebアプリの動作確認・視覚テスト・アクセシビリティ・レスポンシブ・フォームバリデーションを実行し、問題を検出・記録する。
dev-context
by classmethodThis skill should be used when the user asks to "dev-context", "プロジェクトコンテキストを生成", "プロジェクトを分析", "generate project context", "analyze project", "コンテキストを更新". プロジェクトの技術スタック・テストフレームワーク・コーディング規約・アーキテクチャを自動分析し、コンパクトなコンテキストファイルを生成する。
dev-debug
by classmethodThis skill should be used when the user asks to "dev-debug", "テストが失敗する", "ビルドエラーを直して", "デバッグ", "debug failing tests", "fix build error", "エラーを修正", "コンパイルエラー", "環境の問題を解決". テスト失敗、ビルドエラー、環境問題など様々なエラーパターンをカテゴリ別に診断し、最小コンテキストで修正する。
dev-impl
by classmethodThis skill should be used when the user asks to "dev-impl", "タスクを実装", "テストファースト実装", "implement task", "実装を開始", "クイック修正", "quick fix", "dev-impl auth 001". TDDをガードレールとしたテストファースト実装を行う。通常モード（Plan+タスク指定）とクイックモード（直接指示）に対応。
dev-init
by classmethodThis skill should be used when the user asks to "dev-init", "技術スタックを選定", "新規プロジェクト初期化", "initialize tech stack", "プロジェクトをセットアップ", "setup project", "scaffold project", "プロジェクト作成", "tech stackを決める". インタラクティブなヒアリングでプロジェクトの技術スタックを決定し、dev-context互換のコンテキストファイルを生成する。承認制でプロジェクトスキャフォールディングも実行可能。
dev-navigate
by classmethodThis skill should be used when the user asks to "dev-navigate", "どこから始めれば", "何を使えばいい", "スキルを選んで", "ナビ", "navigate", "which skill", "how to start", "開発の進め方", "何から始める". 開発者がやりたいことをヒアリングし、最適なtsumikiスキルとその実行順序をナビゲーションする。
dev-plan
by classmethodThis skill should be used when the user asks to "dev-plan", "実装計画を作成", "要件からタスク分解", "create implementation plan", "plan tasks", "タスクを分割", "設計してタスクにする", "詳細要件定義", "full-spec plan", "EARS要件". ユーザーの要件をインターフェースファースト設計とテスト可能なタスクに分解し、Plan単位でdocs/dev/plans/に保存する。Lightweight（素早い計画）とFull-spec（EARS要件定義付き）の2モードに対応。
dev-run
by classmethodThis skill should be used when the user asks to "dev-run", "自動実装", "タスクを一括実装", "auto implement", "run all tasks", "タスクを自動実行", "バッチ実装", "dev-run auth 001 005". Plan内の指定範囲のタスクをdev-impl/dev-verify/dev-debugのワークフローで自動実行するオーケストレーションスキル。
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.