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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backend-work-planner
by JavaLangRuntimeExceptiondocs/spec/ の仕様書を元に、docs/work/YYYYMMDD_<feature>.md として実装計画書を作成する。Phase 分解・影響範囲・テスト戦略・リスクを構造化し、実装前にユーザー承認を取る。「実装計画立てて」「work 書いて」「どう進めるか計画して」などで起動。
backend-rubber-duck
by JavaLangRuntimeException答えを先に出さず問い返しで思考を整理するソクラテス式の壁打ち相手を務める。バグ調査・設計判断・方針決定で、ユーザー自身が答えに辿り着くのを助ける。「壁打ちして」「rubber duck」「一緒に考えて」「考えを整理したい」などで起動。
backend-spec-creator
by JavaLangRuntimeException新機能の仕様書を docs/spec/ 配下に作成する。実装詳細を含めない純粋な仕様（目的・振る舞い・ルール・境界）に絞ってマークダウン化する。「仕様書作って」「spec 書いて」「〜の仕様まとめて」などで起動。
backend-spec-updater
by JavaLangRuntimeExceptiondocs/spec/ 配下の既存仕様書を新しい決定事項や変更に合わせて更新する。差分をわかりやすく提示し、整合性を保ちながら最小限の変更で書き換える。「spec 更新して」「仕様書を最新化して」などで起動。
backend-dev-manager
by JavaLangRuntimeException汎用的な開発オーケストレーター。docs/spec/ の仕様と docs/work/ の実装計画書を軸に、Phase 分解と PDCA サイクルで実装を進める。自分は実装せず、各 Phase を Task で専門 agent / Explore / Plan に委託する。「開発進めて」「実装オーケストレートして」「機能実装して」などで起動。
backend-integration-test-writer
by JavaLangRuntimeException実 DB を起動して Handler → Usecase → Service → Repository を貫通させる integration test を書く。fixture 分離、TestMain/global setup、トランザクション単位のロールバックによるテスト間隔離、API エンドポイントを HTTP 経由で叩く E2E 的検証を扱う。単体テスト(mock 前提)は対象外で `backend-test-writer` に委譲する。「integration test 書いて」「E2E テスト追加して」「DB 込みのテスト書いて」などで起動。
backend-test-gap-finder
by JavaLangRuntimeException既存コードに対してテストが不足している箇所を体系的に洗い出し、優先度付きリストにする。コード複雑度・変更頻度・障害リスクを軸に評価する。「テスト不足どこ？」「テストギャップ調べて」「カバレッジ穴探して」などで起動。
backend-test-planner
by JavaLangRuntimeException実装前 or 実装後にテスト戦略を立てる。どの層に何をテストするか、ケースの網羅性、既存基盤の活用方針を計画書化する。実装スキルには踏み込まず、テストの設計に集中する。「テスト戦略立てて」「テスト計画書いて」などで起動。
backend-test-writer
by JavaLangRuntimeExceptionテスト計画（test-planner 成果物）または既存の仕様に基づいて、プロジェクト既存パターンに合わせた**単体テスト(mock 前提)**を書く。言語・フレームワーク非依存で、既存テストの構造を踏襲することを最優先する。実 DB を起こす integration test は `backend-integration-test-writer` に委譲する。「テスト書いて」「このコードのテスト追加して」などで起動。
backend-work-planner
by JavaLangRuntimeExceptiondocs/spec/ の仕様書を元に、docs/work/YYYYMMDD_<feature>.md として実装計画書を作成する。Phase 分解・影響範囲・テスト戦略・リスクを構造化し、実装前にユーザー承認を取る。「実装計画立てて」「work 書いて」「どう進めるか計画して」などで起動。
backend-code-reviewer
by JavaLangRuntimeException差分や指定コードを構造化されたレビュー観点（正確性・設計・テスト・可読性・セキュリティ）で読み、優先度付きで指摘する。修正は提案するが勝手に書き換えない。「レビューして」「コードレビュー」「セカンドオピニオン」などで起動。
backend-codebase-explorer
by JavaLangRuntimeException未知のコードベースを最短で把握するための構造化探索を行う。言語・ビルドツール・アーキテクチャ・テスト方針・主要なエントリポイントを短時間でレポート化する。「このプロジェクト教えて」「コードベース調べて」「初見で入ったから概要ほしい」などで起動。
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.