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...
update-docs
by tegnikeaituber-kit-docsのドキュメントサイトを最新バージョンに更新する。バージョンごとの差分を分析し、日本語→英語→中国語の順で3言語のドキュメントを更新する。ドキュメント更新、docs更新、バージョンアップ対応、リリースノート反映などの作業で使用する。
verify-endpoints
by tegnike全AIプロバイダーのAPIエンドポイント（チャット・TTS・STT・Embedding）を動作確認し、レポートを出力する。
sync-translations
by tegnike日本語の翻訳ファイル（ja/translation.json）から他の言語ファイルに不足しているキーを同期し、READMEの変更も多言語READMEに反映する。翻訳キーの追加、翻訳ファイルの同期、i18nキーの更新、READMEの多言語同期時に使用。
remotion-motionpngtuber
by tegnikeAdd MotionPNGTuber / MotionPNGTuber_UI style talking characters and Japanese TTS narration to Remotion or HyperFrames videos. Use when Codex needs to generate dialogue audio with VOICEVOX or AivisSpeech, place the audio on a Remotion or HyperFrames timeline, and render a PNGTuber character using a mouthless video or frame sequence, mouth_track.json, and mouth sprites; fix mouth alignment, green-screened assets, lip-sync timing, or render issues involving MotionPNGTuber in Remotion or HyperFrames.
translate-post
by tegnikeTranslate Japanese blog posts (content/posts/*.md) into natural English, creating -en suffixed files. Use this skill whenever the user asks to translate articles, says "記事を翻訳して", "英語にして", "translate article", "translate to English", "翻訳して", "英訳して", "English version作って", or mentions wanting English versions of blog posts. Also trigger when the user discusses i18n/localization of blog content or asks about making posts available in English.
nikechan-blog-writer
by tegnikeニケちゃん（tegnike）の文体・構成・キャラクター感を再現して、AI/AITuber/LLM/開発ツール系の技術記事やエッセイ記事をこのブログ（nikechan-blog）向けに執筆する。ユーザーが「記事を書いて」「ブログ書いて」「この内容をニケちゃん風に」「技術記事」「Claude Codeについて書いて」「AITuberKitの〇〇について記事にして」など、ニケちゃんの立場で記事を書く依頼をしたときは必ず使うこと。既存の content/posts/ 配下の Markdown として出力する必要があるので、出力先・frontmatter・画像パス規約も含めてこのスキルに従うこと。
translate-post
by tegnikeTranslate Japanese blog posts (content/posts/*.md) into natural English, creating -en suffixed files. Use this skill whenever the user asks to translate articles, says "記事を翻訳して", "英語にして", "translate article", "translate to English", "翻訳して", "英訳して", "English version作って", or mentions wanting English versions of blog posts. Also trigger when the user discusses i18n/localization of blog content or asks about making posts available in English.
nikechan-blog-writer
by tegnikeニケちゃん（tegnike）の文体・構成・キャラクター感を再現して、AI/AITuber/LLM/開発ツール系の技術記事やエッセイ記事をこのブログ（nikechan-blog）向けに執筆する。ユーザーが「記事を書いて」「ブログ書いて」「この内容をニケちゃん風に」「技術記事」「Codexについて書いて」「AITuberKitの〇〇について記事にして」など、ニケちゃんの立場で記事を書く依頼をしたときは必ず使うこと。既存の content/posts/ 配下の Markdown として出力する必要があるので、出力先・frontmatter・画像パス規約・サムネイル情報・挿絵用画像生成プロンプト作成も含めてこのスキルに従うこと。
nikecoin
by tegnikeニケコイン管理スキル。残高確認、贈呈、履歴確認などの操作を行う。「ニケコイン」「残高」「贈呈」などの指示があった場合に参照する。
suno
by tegnikeSuno v5を使った曲作成スキル。「曲を作って」「歌を作って」「Sunoで作って」などの指示があった場合に呼び出す。歌詞・プロンプト・説明文・ジャケット画像プロンプトの4点セットを出力する。
optimize-images
by tegnikeOptimize images in a web project by converting PNG/JPG to WebP format with resizing. Use this skill whenever the user mentions image optimization, page load speed, image compression, converting images to WebP, reducing image file sizes, or asks about large images slowing down their site. Also trigger when the user asks to audit or check image sizes in public/ or static asset directories.
discord-summary
by tegnikeDiscordチャンネル/スレッドの直近または指定期間の会話を、依頼意図に合わせて自然に要約する。
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.