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...
runbook
by agaemo開発タスク（本番デプロイ・DB移行・インシデント対応など）の名前を渡すと、航空SOP方式の読み上げ式チェックリストをMarkdownで生成する。実行フェーズの品質を保証したいときに使う。デプロイ・移行・インシデント対応などの前にチェックリストが欲しいときに自動呼び出しされるべきスキル。
improve-skill
by agaemoスキルファイルを静的解析・実行シミュレーションで改善する。実プロジェクトを動かさずにトークン効率・明瞭性・完全性を評価し、具体的な改善案を提示する。スキルを新規作成・修正したとき、または品質改善を求められたときに使う。
improve-agent
by agaemoエージェントファイルを静的解析・実行シミュレーションで改善する。実プロジェクトを動かさずにトークン効率・役割純粋性・命令明瞭性を評価し、具体的な改善案を提示する。エージェントを新規作成・修正したとき、または品質改善を求められたときに使う。
scorer
by agaemoコードベース全体の健全性を6つの観点で定期評価するとき呼び出す。スコアと改善タスクの一覧を返す。実装は行わない。
release-planner
by agaemo本番リリース前にリリース戦略・デプロイ計画・ロールバック手順を策定するとき呼び出す。カナリア・ブルーグリーン・フィーチャーフラグの選択支援、リリースチェックリストの生成を行う。
qa
by agaemo既存プロジェクトのQA基盤を構築する。テスト方針の策定・フレームワーク導入・優先順位付けによる段階的なテスト実装・CI組み込みまでを一貫して進める。
new-project
by agaemo動的アプリ（API・DB・認証あり）のセットアップ手順。/craft から動的アプリを選択したときに実行される。
iac
by agaemoIaC（Infrastructure as Code）の導入・設計・運用手順。Terraform/OpenTofu を中心に、インフラをコード管理したいときに使う。
git-workflow
by agaemogit/gh操作（状態確認・ブランチ作成・ステージング・コミット・PR作成・マージ）を安全な手順で行う。
retrofit
by agaemoテストが少ない・ない既存システムへのテスト追加ワークフロー。Characterization Test・Seam の導入・TDD への移行を段階的に進める。引数にファイルパスを渡すと対象ファイルへのテスト追加から直接開始できる。
consult
by agaemo既存システムの課題・移行・リファクタについて相談し、選択肢の整理から実行・PRまでを行う。/craft で「既存システムの相談」を選択したときに実行される。
new-static
by agaemoAstro + Node.js で静的サイト（LP・PoC・画面モック）をセットアップする手順。/craft から静的サイトを選択したときに実行される。
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.