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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rmodp-computational-view-web
by atanakaRM-ODP Computational Viewpoint Specification（計算視点仕様書）を導出する。 Enterprise Viewpoint および Information Viewpoint の仕様を入力として、 ITU-T X.903 / ISO/IEC 10746-3 に基づく5ステップの構造化分析 （Computational Object → Interface → Interaction/Binding → Environment Contract/Distribution Transparency → Refinement）を行い、 PlantUML コンポーネント図・シーケンス図（PNG 生成）を含む Markdown ドキュメントを生成する。 Use when: RM-ODP Computational Viewpoint の仕様を作成する場合、 システムの機能分割・インターフェース・分散透過性を体系的にモデリングする場合。
rmodp-correspondence-web
by atanakaRM-ODP Correspondence Specifications（対応関係仕様書）を導出する。 作成済みの5つの Viewpoint Specifications を入力として、 ITU-T X.903（第10章）および ITU-T X.911（第11章）に基づく4ステップの構造化分析 （Enterprise 起点の対応関係 → 基本3視点間の対応関係 → トレーサビリティ検証 → Refinement） を行い、視点間の整合性を検証した Markdown ドキュメントを生成する。 Use when: 全5 Viewpoint 作成後に視点間の整合性を検証する場合、 トレーサビリティマトリクスを作成し孤立要素や機能欠落を検出する場合。
rmodp-engineering-view-web
by atanakaRM-ODP Engineering Viewpoint Specification（エンジニアリング視点仕様書）を導出する。 Computational Viewpoint 仕様とインフラ環境の前提条件を入力として、 ITU-T X.903 / ISO/IEC 10746-3 に基づく5ステップの構造化分析 （Node/Nucleus → Capsule/Cluster/BEO → Channel → Distribution Transparency → Refinement） を行い、PlantUML デプロイメント図・Channel 構成図（PNG 生成）を含む Markdown ドキュメントを生成する。 Use when: RM-ODP Engineering Viewpoint の仕様を作成する場合、 分散システムの物理配置・通信経路・透過性実現を体系的にモデリングする場合。
rmodp-enterprise-view-web
by atanakaRM-ODP Enterprise Viewpoint Specification（企業視点仕様書）を導出する。 業務概要と業務シナリオを入力として、ITU-T X.911 / ISO/IEC 15414 に基づく 6ステップの構造化分析（Community/Objective → Role/Enterprise Object → Behaviour → Deontic Concepts → Policy/Accountability → Refinement）を行い、 Markdownドキュメントを生成する。 Use when: RM-ODP Enterprise Viewpoint の仕様を作成する場合、 業務シナリオから Community/Role/Policy 等を体系的にモデリングする場合。
rmodp-information-view-web
by atanakaRM-ODP Information Viewpoint Specification（情報視点仕様書）を導出する。 業務概要と業務シナリオを入力として、ITU-T X.903 / ISO/IEC 10746-3 に基づく 5ステップの構造化分析（Information Object → Invariant Schema → Static Schema → Dynamic Schema → Refinement）を行い、PlantUML クラス図・制約図・状態遷移図 （PNG 生成）を含む Markdown ドキュメントを生成する。 Use when: RM-ODP Information Viewpoint の仕様を作成する場合、 業務データの構造・制約・状態遷移を体系的にモデリングする場合。
rmodp-technology-view-web
by atanakaRM-ODP Technology Viewpoint Specification（技術視点仕様書）を導出する。 Engineering Viewpoint 仕様と技術・非機能要件の前提を入力として、 ITU-T X.903 / ISO/IEC 10746-3 に基づく4ステップの構造化分析 （Technology Object マッピング → Implementable Standard 選定 → Conformance/IXIT → Refinement）を行い、PlantUML 技術スタック図（PNG 生成）を含む Markdown ドキュメントを生成する。 Use when: RM-ODP Technology Viewpoint の仕様を作成する場合、 具体的な技術選定・標準適合性・テスト要件を体系的にモデリングする場合。
rmodp-workflow-web
by atanakaRM-ODP 全6スキル（5 Viewpoint + Correspondence）をオーケストレーションする統合ワークフロー。 業務シナリオから Enterprise → Information → Computational → Engineering → Technology → Correspondence の順に仕様を導出し、視点間の整合性を検証する。フル実行・ステップ指定・再開・検証のみの4モードに対応。 Use when: RM-ODP の複数 Viewpoint を一貫して開発する場合、 「rmodp-workflow-web」「RM-ODP全体」「フルパイプライン」等の指示を受けた場合。
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.