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...
version-bump
by skanehiraCargo.tomlのバージョンをセマンティックバージョニングに従ってバンプアップし、git tagを作成する。前回のgit tagからの差分を分析してバージョン種別（major/minor/patch）を自動判定する。「バージョンを上げて」「リリースして」「version bump」「バージョンアップ」などのリクエストで起動。
requirements-ddd-modeling
by skanehiraドメインエキスパートとの対話を通じてユビキタス言語（用語集）とドメインモデルを作成する。新規プロジェクト開始時のドメイン理解、既存システムのリファクタリング前のモデル整理、チーム内での用語統一が必要な場合に使用。「DDDでモデリングしたい」「ドメインモデルを作成」「用語集を整理」「ユビキタス言語を定義」などのリクエストで起動。
implementation-planning-tasks
by skanehira承認済みの設計書（DESIGN.md）からTDD準拠のTODO.mdを作成します。analyzing-requirementsスキルで設計が完了・承認された後に使用します。developingスキルで実装できる形式のタスクリストを生成します。
implementation-writing-tests
by skanehiraTDD方法論に従ってテストを作成します。テスト対象コードの分析、AAA/Given-When-Thenパターンの適用、正常系・エッジケース・エラー系のカバレッジを確保します。「テストを書いて」「テストを作成」「単体テストを追加」などのリクエストで起動します。
requirements-analyzing-requirements
by skanehiraユーザー要件を分析し、システム設計ドキュメント(DESIGN.md)を生成します。ユーザー要件が曖昧または不明確な場合、システムアーキテクチャの設計が必要な場合、大規模な機能開発の設計仕様が必要な場合、技術的実現可能性の検証が必要な場合に使用します。不明点はAskUserQuestionツールで確認します。
requirements-feasibility-check
by skanehira技術的な実現可能性を検証し、PoCの計画を立てる。ユースケース記述後、DDDモデリングや本格実装の前に技術リスクを洗い出す場合に使用。「技術的に実現できるか確認したい」「PoCを計画したい」「技術リスクを洗い出したい」「不確実性を検証したい」などのリクエストで起動。
requirements-interview
by skanehiraDESIGN.mdを読み込み、技術実装・UI/UX・懸念点・トレードオフについて深掘りインタビューを実施し、仕様をDESIGN.mdに書き出す
requirements
by skanehira要件・設計フェーズを実行。requirements-user-story → requirements-ui-sketch → requirements-usecase-description → requirements-feasibility-check → requirements-ddd-modeling → requirements-analyzing-requirements を順次実行し、DESIGN.md を生成。「設計フェーズを開始」「要件を整理したい」「/requirements」などで起動。
requirements-ui-sketch
by skanehira画面構成とユーザーフローを整理し、ワイヤーフレームを作成する。ユーザーストーリー作成後、実装前にUIの方向性を固めたい場合に使用。「UIを整理したい」「画面構成を考えたい」「ワイヤーフレームを作りたい」「ユーザーフローを可視化」などのリクエストで起動。
requirements-usecase-description
by skanehiraユーザーストーリーを詳細なユースケース記述に展開する。正常系・異常系・代替フロー、ビジネスルールを明確化する。ユーザーストーリー作成後、DDDモデリングや設計の前に使用。「ユースケースを詳細化したい」「フローを整理したい」「異常系を洗い出したい」「ビジネスルールを明確にしたい」などのリクエストで起動。
requirements-user-story
by skanehiraユーザーストーリーを作成し、優先順位付けを行う。SLCでスコープが決まった後、実装計画を立てる前に使用。「ユーザーストーリーを書きたい」「機能を整理したい」「優先順位をつけたい」「バックログを作りたい」などのリクエストで起動。
slide-generating
by skanehiraテキスト入力からWebベースのプレゼンテーションスライド（HTML + Tailwind CSS + JS）を生成する。カンファレンス登壇やセールス資料として使えるレベルの品質を目指す。「スライドを作って」「プレゼン資料を作成」「デッキを生成」「/slide-generator」、既存のスライドHTMLの修正依頼で起動する。
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.