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...
xs-arxiv
by karaage0703arXiv論文の検索・トレンド発見・詳細分析を行う統合スキル。興味度スコアでユーザーに刺さる論文を自動選定。PDF全文読み込み・Notion蓄積対応。「arXivチェックして」「論文検索」「論文分析して」で使用。
xs-bridge-ideas
by karaage0703ワークスペース内の遠い知識や活動をつないで、新しいアイデアを生成する思考支援スキル。過去記事・メモ・RAG・発信資産から企画や実装ネタを出したい時に使用。「アイデア出して」「知識をつないで」「一見関係ないものをつないで」で使用。
xs-diagram-generator
by karaage0703内容に応じて最適な図表形式を自動選択し、ローカルで画像生成（Pillow）またはコード出力（Mermaid/PlantUML）。フローチャート、アーキテクチャ図、階層図などを生成。資料作成、システム設計時に使用。
xs-google-workspace
by karaage0703gogcli経由でGmail・Google Drive・Google Calendarを操作するスキル。複数Googleアカウント対応。「メールチェック」「メール確認して」「Driveにアップロード」「Driveのファイル」「Gmailで検索」で使用。
xshealth-advisor
by karaage0703食事・運動の記録と健康管理アドバイスを行うスキル。食事報告からカロリー概算、運動記録、週次レポートを生成。データはmemory/に記録。「健康チェック」「週次ヘルスレポート」「食事記録して」「運動記録して」で使用。
xs-multi-agent
by karaage0703複数のAIエージェント（Claude/Codex/Gemini）で協調して考える・調査する・レビューする。「みんなで考えて」「マルチエージェントで分析して」「複数のAIでレビューして」と言われたら使う。
xs-notion-manager
by karaage0703Notion APIでページ検索・閲覧・作成、ファイルアップロード、画像付き日記作成、個別ブロックの更新・削除を行うスキル。「Notionで検索して」「Notionに日記書いて」「Notionにファイルアップロードして」「Notionの個別ブロックを修正して」で使用。
xs-skill-creator
by karaage0703スキルの作成・改善を行うスキル。既存スキルの分析から抽出したパターンとテンプレートで統一感のあるスキルを効率的に作成。スキルを作りたい、新しいスキルを追加したい、スキルを改善したい、SKILL.mdを書きたい場合に使用。
xs-spontaneous-talk
by karaage0703AIアシスタントが自発的に話しかけてくるスキル。 cronで定期発動し、確率判定で発話するか決める。 チャンネルの会話履歴・日記・メモリをもとに話題を選ぶ。 「話しかけて」「spontaneous-talk」で手動発動も可能。
xs-workspace-rag
by karaage0703ワークスペース全体をベクトル検索＋構造化ファクト管理する常駐サーバー（port 7890、約70ms）。会話で過去の記憶を参照する必要がある時、ファクトを ADD/UPDATE/DELETE する時、両方ともこのスキルを使う。「前に話した」「あの時の」「ワークスペース検索して」「RAGで探して」「ファクト登録」で使用。
xs-xangi-kaizen
by karaage0703xangi 上で起きた事象（再投稿・cron が回らない・データ不整合・想定外の動作・エラー等）の調査・根本原因分析・横展開・修正・報告を、再現性のあるワークフローで実行する汎用スキル。「調査して」「原因調べて」「再発防止」「横展開して」「xangi-kaizen」「事象を分析して」で発動。
xs-xangi-onboarding
by karaage0703xangi の新しいインスタンスを 3 モード（ブラウザ / Discord / Docker フル compose）から選んで立ち上げ、初心者の定着まで伴走するスキル。最初に用途を確認してから分岐する。推奨ワークスペース ai-assistant-workspace の自動展開、初回 BOOTSTRAP、魅力体感ツアー、おすすめカスタマイズ、次の一歩までをガイドする。上級者向けにスタックチャン連動（モード D）もサポート。「xangi 入れたい」「xangi セットアップ」「xangi 始めたい」「xangi-onboarding」「新しい xangi インスタンス立てたい」で発動。
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.