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...
iterate-plan
by jacolaIterate on existing implementation plans with thorough research and updates. Use when you need to modify, expand, or refine an existing plan based on new information or feedback.
jp-describe-pr
by jacolaコード変更を分析し、検証テストを実行し、構造化されたドキュメントを作成して包括的なPR説明を生成します。プルリクエストの説明を作成または更新する必要がある場合に使用します。
jp-implement-plan
by jacola技術計画をフェーズごとに検証しながら実装します。承認された実装計画があり、体系的に実行する必要がある場合に使用します。
jp-iterate-plan
by jacola徹底的な調査と更新で既存の実装計画を反復します。新しい情報やフィードバックに基づいて既存の計画を変更、拡張、または改善する必要がある場合に使用します。
jp-local-review
by jacolagit worktreeを使用して同僚のブランチをレビューするためのローカル環境をセットアップします。現在の作業を中断せずに他の人のコードをレビューする必要がある場合に使用します。
jp-research-codebase
by jacola並行サブエージェントを生成して調査結果を統合することで、コードベース全体にわたる包括的な調査を実施して質問に回答します。コードの動作方法の理解、コンポーネントの場所の特定、アーキテクチャの文書化、またはシステム間の接続のトレースを行う場合に使用します。
jp-validate-plan
by jacola実装を計画と照合して検証し、成功基準を確認し、問題を特定します。計画を実装した後、すべての要件が正しく満たされたことを確認するために使用します。
jp-ci-commit
by jacolaユーザー確認なしでセッション変更のgitコミットを自動作成します。CI/自動化ワークフローで対話的な承認なしにコミットを作成する場合に使用します。
jp-commit
by jacola明確でアトミックなメッセージでセッション変更のgitコミットを作成します。コーディングセッション中の変更をベストプラクティスに従ったコミットメッセージでコミットする必要がある場合に使用します。
jp-create-handoff
by jacola作業を別のセッションに引き継ぐための引き継ぎドキュメントを作成します。セッション終了時に後で継続するための進捗を文書化する必要がある場合に使用します。
jp-create-plan
by jacolaインタラクティブな調査と反復を通じて詳細な実装計画を作成します。新機能、リファクタリング、または実装前に慎重な計画が必要な複雑なタスクを開始する場合に使用します。
jp-debug
by jacolaログ、データベースの状態、git履歴、コードを調査して変更を加えずに問題をデバッグします。何かが壊れていて根本原因を調査する必要がある場合に使用します。
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.