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...
auto-review-fix
by 0tarofPRのBotレビュー（Greptile、Devin）を監視し、レビュー指摘への対応・再レビュー依頼を自動化するスキル。「レビュー対応して」「レビュー待って」「PRのレビュー見て」「Greptileの指摘直して」「レビュー修正」などの依頼で起動。また「Greptileにレビュー依頼」「レビューリクエスト」「Greptileレビューして」などのレビュー依頼にも対応。PRを作成した後のレビュー依頼・対応フローに使う。
ghostty-configure
by 0tarofGhosttyターミナルエミュレータの設定・カスタマイズを支援。テーマ、フォント、キーバインド、ウィンドウ設定など。「Ghostty」「ゴースティ」「ターミナル設定」などの依頼で起動。
nix-rebuild
by 0tarofdotfiles リポジトリの Nix 設定変更をコミットして `nix-rebuild` を走らせ、失敗時はエラーを解析して修正し、最終的に成功したら `git push` まで自動でやり切るスキル。「nix-rebuild して」「設定反映して」「rebuildして」「nix変更したからrebuild」「dotfilesに反映」「Nix適用」などの依頼で起動する。Nix flake はコミット済みの状態しか見ないので「変更したけど反映されない」問題を含めて、コミット忘れによる事故を防ぐためにも積極的に使う。
sibling-repo
by 0tarofメインワークツリーの兄弟リポジトリを参照・操作するスキル。Worktreeで作業中でもメインワークツリーの並列にあるリポジトリのコード参照、 CLAUDE.md確認、API/型定義の参照、Git状態確認、Issue作成、fetch/pullなどが可能。 「しぶりん」「兄弟リポ」「隣のリポ」「sibling repo」「他のリポ」「別リポ」「〇〇リポ見て」「〇〇リポのコード」 「〇〇リポのIssue作って」「〇〇リポをfetch」「〇〇リポのブランチ確認」「隣のプロジェクト」 「関連リポ」などの依頼で起動。「しぶりん」はsibling repoの愛称。リポ名が文脈に出てきたら積極的に使う。
snowflake-query
by 0tarofユーザーの自然言語指示を受け取り、Snowflakeで実行するSQLクエリを生成・実行し、結果をCSV形式で出力します。「Snowflakeで〜を取得して」「〜のデータを表示して」などのリクエストで自動的に起動します。
claude-worktrees
by 0tarof現在のgitリポジトリのworktree一覧を、各worktreeのブランチ・関連PR・Claude Codeセッション情報・再開コマンドと一緒に表示するスキル。「ワークツリー一覧」「アクティブなClaude」「worktree list」「worktree表示」「再開コマンド」「claude worktree」「claudeの状態」「並行作業の状態」などの依頼で必ず起動すること。複数のworktreeでClaude Codeを並行で動かしている時に、どのworktreeで何が進んでいるかを俯瞰するのに使う。
difit
by 0tarofAfter completing the requested implementation, use the difit command to ask the user for a code review.
git-rebase
by 0tarofgit rebaseをノンインタラクティブで実行。「rebase」「リベース」などの依頼で起動。
github-activity
by 0tarofGitHubの認証済みユーザーの今日のアクティビティを取得し、ざっくりまとめるスキル。「今日のアクティビティ」「GitHub activity」「アクティビティまとめ」「what did I do today」「今日何した」「今日の作業まとめ」などの依頼で起動。
my-open-prs
by 0tarof自分のオープンPRを差分サイズ順にリスト表示。デフォルトでドラフトPR・アーカイブ済みリポジトリを除外。「自分のPR」「open PR」「PRリスト」「PR一覧」「出してるPR」などの依頼で起動。
auto-review-fix
by 0tarofInspect and address PR bot reviews from Greptile and Devin. Use when the user asks to handle bot review feedback, wait for review completion, check PR review status, fix Greptile or Devin comments, request Greptile review, or continue a PR review loop after opening a pull request.
nix-rebuild
by 0tarofdotfiles リポジトリの Nix 設定変更をコミットして `nix-rebuild` を走らせ、失敗時はエラーを解析して修正し、最終的に成功したら `git push` まで自動でやり切るスキル。「nix-rebuild して」「設定反映して」「rebuildして」「nix変更したからrebuild」「dotfilesに反映」「Nix適用」などの依頼で起動する。Nix flake はコミット済みの状態しか見ないので「変更したけど反映されない」問題を含めて、コミット忘れによる事故を防ぐためにも積極的に使う。
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.