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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yomiage-overview
by shuhei1101VoiceBox APIサーバー（yomiage-svr）プロジェクトの包括的な概要。VOICEVOX音声合成、Ollama口調変換、FastAPI HTTPサーバーを組み合わせたキャラクターボイス読み上げシステムの全体構成、技術スタック、セットアップ手順を提供します。
claude-kit-rule-creator
by shuhei1101Create a new path-scoped rule under .claude/rules/ using the step-based structure. Trigger when the user says "新しいルール作って", "ルールを新規作成", "make a rule for X", or "create a rule for".
claude-kit-skill-creator
by shuhei1101Create a new Claude Code skill under .claude/skills/ using the step-based structure. Trigger when the user says "スキルを作りたい", "新しいスキル作って", "create a skill", "make a skill for X", or claude-kit dispatches here.
claude-kit-claude-creator
by shuhei1101Create or overhaul a CLAUDE.md (and its CLAUDE.jp.md mirror) for a project or subfolder. Trigger when the user says "CLAUDE.md を作って", "CLAUDE.md を書いて", "create a CLAUDE.md", "クロードのガイドを作りたい", "このフォルダの CLAUDE.md を作って", or asks to set up Claude Code instructions for a project or specific folder.
claude-kit-claude-refactor
by shuhei1101Audit and organize Claude configuration (rules / skills / CLAUDE.md / hooks). Trigger when the user says "ルールを整理して", "設定が肥大化してきた", "スキルに重複がある気がする", "CLAUDE.md が長くなってきた", ".claude/ をきれいにしたい", or calls `/claude-kit:claude-refactor` explicitly.
claude-kit-env-sync
by shuhei1101Sync Claude Code configuration files between WSL and Windows environments. Scans both sides, shows a diff, and copies selected files after user confirmation. Trigger when the user says "WSL と Windows の設定を同期して", "env-sync して", "Claude Code の設定をコピーしたい", "設定ファイルを移行したい", or invoked explicitly as `/claude-kit:env-sync`.
claude-kit-hook-creator
by shuhei1101Create a prompt-injection hook — a hook that injects a text prompt into Claude's context at a specific event. Trigger when the user says "I want to give Claude instructions at a specific moment", "inject a prompt on hook", "create a hook that tells Claude to do X when Y happens", "hook でプロンプトを差し込みたい", "特定のタイミングで AI に指示を出したい", or invoked explicitly as `/claude-kit:hook-creator`.
claude-kit-jp-mirror-sync
by shuhei1101Accept one or more Japanese mirror (.jp.md) files and create or update the corresponding English counterparts (.md). Treats the JP mirror as the source of truth and spawns one claude-kit:jp-mirror-translator subagent per file, running all subagents in parallel. Trigger when the user says "JP ミラーを同期して", "英語版を更新して", "jp-mirror-sync して", "translate JP mirror", or passes one or more .jp.md file paths.
claude-kit-plugin-config
by shuhei1101When /claude-kit:plugin-config is invoked. Or when the user says "設定を変えたい", "env を設定したい", "トグルを切り替えたい", "JP ミラーを無効にしたい", "注入言語を変えたい".
claude-kit-plugin-creator
by shuhei1101Create or update a Claude Code plugin with versioning (changelogs/ folder). Trigger when the user says "新しいプラグインを作りたい", "プラグインを作って", "プラグインを更新したい", "create a plugin", "update a plugin", "make a new plugin", or "plugin-creator して".
claude-kit-plugin-migrate
by shuhei1101Walk the project's claude-kit-authored artifacts (`.claude/skills/**` / `.claude/rules/**` / `.claude/hooks/**` / `**/CLAUDE.md` / `**/.claude-plugin/{plugin,marketplace}.json`) and bring them in line with the currently installed claude-kit reference conventions, applying minimal in-place edits where they deviate. Also re-applies the statusline if claude-kit's version is currently set in `~/.claude/settings.json`. Manual invocation only — use /claude-kit:plugin-migrate.
claude-kit-statusline-setup
by shuhei1101Use this skill to configure the user's Claude Code status line setting. Trigger when the user says "set up the status line", "configure statusline", "apply statusline", "ステータスラインを設定して", "ステータスラインをセットアップして", or invoked explicitly as `/statusline-setup`.
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.