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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obsidian-bases
by 666666ma999999Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files or when the user mentions Bases, table/card views, filters, or formulas in Obsidian. Triggers (kepano bundle): 「obsidian-skills を使って」「.base ファイル」「Bases」「テーブルビュー」「フィルター式」「ノートDB」.
json-canvas
by 666666ma999999Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian. Triggers on (kepano-obsidian-skills bundle): 「obsidian-skills を使って」「obsidian-skills」「.canvas ファイル」「キャンバス作成」「JSON Canvas」「マインドマップ」「フローチャート」「ノードとエッジ」「Obsidian キャンバス編集」.
obsidian-cli
by 666666ma999999Interact with Obsidian vaults via the Obsidian CLI to read, create, search, and manage notes, plus plugin/theme development (reload plugins, run JS, screenshots, inspect DOM). Use for vault CLI operations or Obsidian plugin debugging. Triggers (kepano bundle): 「obsidian-skills を使って」「obsidian CLI」「vault 操作」「Obsidian プラグイン開発」「DOM を調べて」.
obsidian-markdown
by 666666ma999999Create and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in Obsidian or when the user mentions wikilinks, callouts, frontmatter, tags, or embeds. Triggers (kepano bundle): 「obsidian-skills を使って」「Obsidianノート編集」「ウィキリンク」「コールアウト」「frontmatter 整える」「.md 編集」.
defuddle
by 666666ma999999Extract clean markdown content from web pages using Defuddle CLI, removing clutter and navigation to save tokens. Use instead of WebFetch when the user provides a URL to read or analyze, for online documentation, articles, blog posts, or any standard web page. Do NOT use for URLs ending in .md — those are already markdown, use WebFetch directly. Triggers on (kepano-obsidian-skills bundle): 「obsidian-skills を使って」「obsidian-skills」「URL を読んで」「記事を取り込んで」「記事を抽出」「ブログを取得」「ウェブページをきれいに」「Webページから本文だけ」「ノイズ除去」「defuddle」.
fe-be-integration
by 666666ma999999FE/BE統合アーキテクチャのコア原則と概要。 詳細な実装パターンはサブスキル（fe-be-phase1-4, fe-be-phase5-9, fe-be-advanced）を参照。
x-scraping
by 666666ma999999X(Twitter)からツイートを安全に収集するスキル。bot検知を回避しながらPlaywrightでスクレイピングを実行。 Cookieベース認証、人間らしい操作パターン、いいね数フィルタリングに対応。 キーワード: X, Twitter, スクレイピング, ツイート収集, インフルエンサー
autoresearch
by 666666ma999999Autonomous iterative research loop. Takes a topic, runs web searches, fetches sources, synthesizes findings, and files everything into the wiki as structured pages. Based on Karpathy's autoresearch pattern: program.md configures objectives and constraints, the loop runs until depth is reached, output goes directly into the knowledge base. Triggers on: "/autoresearch", "autoresearch", "research [topic]", "deep dive into [topic]", "investigate [topic]", "find everything about [topic]", "research and file", "go research", "build a wiki on".
execution-patterns
by 666666ma999999実行パターン詳細ガイド。SubAgent委託テンプレート（5項目必須）、コンテキスト予算チェック （100行超→Extract-to-File）、データ分析委託パターン（Phase分割）、デバッグ鉄則 （3-Fix Limit、根本原因特定まで修正禁止）、リファクタリング戦略（参考コード方式）。 SubAgent委託時、デバッグ時、大量データ分析時、リファクタリング時に使用。 キーワード: SubAgent, 委託, デバッグ, リファクタリング, データ分析, Extract-to-File NOT for: 単純なタスク実行、1ファイル修正、デバッグの根本原因分析手法のみ（→ debugging-guide）、リファクタリング戦略そのもの（→ refactoring-guide）
fetch-engagement
by 666666ma999999influxプロジェクトのVNCコンテナ経由でX(Twitter)投稿のエンゲージメント（likes/views/retweets/replies/bookmarks）を取得する。単体URL・URLリスト・候補Markdown抽出に対応。複数アカ対応（`--account`）。取得結果を素材テーブルに自動追記できる。
sales-analysis
by 666666ma999999売上データの多変数分析スキル。カテゴリ・担当者・商品属性など複数変数が絡む売上データを分析し、各変数の影響度を正確に測定する。 使用タイミング: (1) 売上に影響する要因を特定したい (2) 複数変数の影響度を比較したい (3) 交絡因子を除去してフェアな評価をしたい (4) 商品力・担当者力・カテゴリ効果を分離したい キーワード: 売上分析、重回帰、特徴量重要度、交絡因子、係数算出 単純な合計・平均計算や単一変数の分析には使用しない。
skill-creator
by 666666ma999999会話のパターンやワークフローを再利用可能なスキルとして保存するスキル。 新規スキルの作成手順とベストプラクティスを提供。 キーワード: スキル作成, ワークフロー保存, パターン化
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.