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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zsh-bench
by 844196zsh の対話シェルとしての user-visible なレイテンシ (起動時間、コマンド実行時の重さ、キー入力の追従性) を zsh-bench で計測し、結果を人間の知覚閾値と照らして評価する。「`.zshrc` を最適化したい」「プラグインを足した/削った前後で性能を比較したい」「zsh-bench を走らせたい」といった zsh のパフォーマンス系のリクエストでは必ずこのスキルを使う。
headless-commit
by 844196ステージ済みの変更からコミットメッセージを自動生成し、コミットする (ヘッドレスモード)。
pr
by 844196PR 作成依頼があったら必ず使うスキル。「PR 作って」「PR 出して」「pull request 作成」「プルリク作って」「PR 上げて」等の指示を受けたら、このスキルを呼び出して PR を作成する。直接 `gh pr create` を実行せずこのスキルを経由すること。
handover
by 844196現セッションの内容を整理・文書化してファイルへ書き出し、後で読み返して作業を再開できるようにする。
handover-tmux
by 844196現セッションの内容を整理・文書化してファイルへ書き出し、新しく tmux で立ち上げる Claude Code セッションに作業を引き継ぐ。
takeover
by 844196ハンドオーバー文書を読んで作業を引き継ぐ。
using-mise-tasks
by 844196mise タスク機能の使い方。以下のいずれかに当てはまる作業では必ずこのスキルを参照する — モデルが学習データで混同しがちな mise 固有の構文を避けるため: - `mise run` でのタスク実行（引数・フラグの渡し方。`--` は `-q`/`-v`/`-h`/`--help` などの予約フラグ pass-through 専用） - `mise.toml` の `[tasks.*]` の読み書き（`run`、`depends`、`depends_post`、`usage`、`sources`、`outputs`、`tools`、`dir`、`raw`） - ファイルタスク (`.mise/tasks/`、`mise-tasks/`、`.config/mise/tasks/` 等) の作成・編集（shebang、実行権限、`#MISE` / `#USAGE` ディレクティブ） - monorepo タスク — `experimental_monorepo_root`、`//path:task` 絶対指定、`//packages/...:*` のパス×タスクワイルドカード、パッケージ間依存 - `mise tasks --all --json` などタスク調査コマンドの使い分け 対象外（別機能）: `mise use`/`mise install` などの tool・version 管理、shim 設定、top-level `[env]` セクション、`mise.lock` の運用、`.mise.toml` 以外の設定ファイル配置。
receive
by 844196他の Claude Code セッションから P2P メッセージ (p2p monitor の task-notification、JSON 形式) を受信したときの解釈・全文復元・対応方針。
send
by 844196他の Claude Code セッションへ P2P メッセージを送る。ユーザーが送信先のセッション ID を示したうえで、そのセッションのエージェントに指示・質問・情報を伝えたい / 聞きたいとき —— 例えば「d3e37001-… のセッションでやってる内容だから、向こうのエージェントに聞いてみて」のように `p2p` や `claude-p2p` の語が無くても —— このスキルを使う。受信した P2P メッセージに返信するときにも使う。送信先セッション ID は常にユーザーが与える (エージェントが peer を探すことはない)。Slack・メール・git 通知などセッション間通信でない送信や、受信側の処理 (p2p:receive スキル) は対象外。
recall-commands
by 844196atuin に記録された Claude Code の Bash 実行履歴をサルベージするための「正しいクエリ組み立て方」リファレンス
update-mise
by 844196mise を最新または指定バージョンにアップデートする。「mise をアップデート」「mise を最新に」「mise を X.Y.Z に」といった指示で発動する。
update-claude
by 844196Claude Code を最新または指定バージョンにアップデートし、公式チェンジログを要約する。「claude をアップデート」「claude を最新に」「claude を X.Y.Z に」といった指示で発動する。
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.