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...
shiiman-googleapps-script
by shiimanGoogle Apps Script プロジェクトを新規作成・コード更新する。「GAS 作成」「Apps Script 作成」「スクリプト作成」「GAS 更新」「Apps Script 更新」「スクリプト編集」「コードを更新」などで起動。
shiiman-googledrive-search
by shiimanGoogle Drive を検索する。「Drive を検索」「ドライブ検索」「ファイルを探して」「Drive で検索」「Google Drive 検索」「ファイル名で検索」「条件で検索」などで起動。
shiiman-googlegmail-unread-mark
by shiimanGmail の未読を既読化する。「既読にする」「未読を既読」「メールを既読化」「Gmail 既読化」「未読を消す」「メールを開封扱い」「一括既読」などで起動。
shiiman-slackprofile-update
by shiimanSlack プロフィールを更新する。「プロフィール更新」「ステータス変更」「表示名を変更」「自分のステータス」「プロフィールを変更」「ステータス設定」などで起動。ユーザートークン（SLACK_USER_TOKEN）が必要。
shiiman-slackunread-check
by shiimanSlack の未読メッセージを確認する（全チャンネル横断確認対応）。「Slack未読確認」「未読メッセージ」「未読ある？」「Slackの未読」「未読を見せて」「未読チェック」「未読メール確認」「全チャンネルの未読」「未読サマリー」などで起動。Pythonスクリプト `slack_message.py unread` を使用。
shiiman-slackunread-mark
by shiimanSlack チャンネルを既読にする（全チャンネル一括既読化対応）。「既読にして」「既読化」「チャンネル既読」「未読を消す」「既読マーク」「全部読んだことにして」「既読にしたい」「全部既読」「一括既読」などで起動。Pythonスクリプト `slack_message.py mark-read` を使用。
shiiman-slackuser-setup
by shiimanSlack のデフォルトユーザーを設定する。「自分を設定」「ユーザー設定」「デフォルトユーザー設定」「Slackユーザー登録」「自分のIDを設定」「自分のSlackを設定」などで起動。
shiiman-workflow-agent-team
by shiimanAgent Team（tmux + TeamCreate）で並列実装する開発フロー。「エージェントチームフロー」「workflow-agent-team」「Agent Team で実装」「チームで実装」「チーム並列開発」などで起動。Issue/PR まで作るかは発話・引数から判断し、曖昧なら確認する。
eyecatch-create
by shiimanブログ記事の内容からアイキャッチ画像を生成する。『アイキャッチを作成』『画像を再生成』『/eyecatch-create』などで起動。google-genmedia-mcp の generate_image を使って assets/eyecatch.png を作成。
eyecatch-create
by shiimanブログ記事のアイキャッチ画像を生成する。「アイキャッチ作成」「eyecatch」「サムネイル生成」「アイキャッチ画像」などで起動。記事の内容を分析し、統一デザインルールに基づいた高品質な画像を生成。
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.