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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unity-runtime-bug-hunt
by moorestechUnity Editor PlayMode 中のランタイム挙動不具合・例外・応答不能の原因特定スキル。uloop execute-dynamic-code でランタイム状態を広く浅くダンプし、JetBrains Rider debugger で狭く深く変数/BPヒットを観測する。TRIGGER when: (1) ユーザーのメッセージに PlayMode 実行時のランタイム例外スタックトレースが貼られている（NullReferenceException / TimeoutException / IndexOutOfRangeException 等）(2) 「〇〇するとエラー/例外が出る」「破壊/生成したら固まった」などプレイ操作と異常の因果が絡む (3) クライアント-サーバー通信のタイムアウト・応答 null・"Receive null"・パケット未返信・サーバー固まり (4) 「動かない」「反応しない」「呼ばれない」「途中で止まる」「期待した動きにならない」「なぜ〇〇にならない？」といった実行フロー系 (5) 「ランタイム状態/実行中のインスタンスを見て」「実行中のコンポーネント状態を確認」 (6) null の直接源は分かるが、なぜその値が null かがランタイム状態依存（別スレッド/サーバー側/非同期/他インスタンス）。ユーザーが "debug" / "breakpoint" を明示しなくても上記いずれかに該当すれば起動する。SKIP when: (a) コンパイルエラーや型エラー（静的バグ） (b) null の原因が静的初期化漏れと即特定できる（フィールド未初期化がソース読むだけで自明等） (c) テスト入力値の誤りなどコード読解だけで完結するケース。迷ったら起動する（起動コスト低、起動漏れコスト高）。
recipe-chain-machine-count
by moorestechUse when the user asks how many machines are needed to produce a moorestech item at a target rate per minute (e.g. "鉄のフレームを分間5個作るには何台必要?", "X個/分作るとき機械いくつ?"). Walks the recipe DAG from the target item all the way down to raw ores/logs, computes per-step machine counts at base RPM / rated electric power, deduplicates shared intermediates, and outputs a hierarchical tree plus per-machine-type totals and raw-material throughput. Triggers on phrases like 「機械何個」「何台必要」「ライン設計」「中間素材含めて」「分間N個作りたい」.
validate-schema
by moorestechスキーマ編集後のバリデーション実装チェックスキル。foreignKeyを持つプロパティを追加した際にC#バリデーションの追加漏れを防ぐ。 Use when: 1. VanillaSchemaのYAMLファイルにforeignKeyを持つプロパティを追加した後 2. BlockParamや他のマスターデータにGuid参照を追加した後 3. スキーマ編集の完了確認時 4. 「バリデーションチェック」「validate-schema」と言われた時
train-system-notes
by moorestechApply core train invariants for rail graph topology, front/back node semantics, rail position ordering, docking references, reverse behavior, deterministic distance handling, and TrainUnit snapshot boundaries. Use when implementing or debugging train movement/topology consistency and directional rail semantics.
uloop-focus-window
by moorestechBring Unity Editor window to front via uloop CLI. Use when you need to: (1) Focus Unity Editor before capturing screenshots, (2) Ensure Unity window is visible for visual checks, (3) Bring Unity to foreground for user interaction.
master-asset-converter
by moorestechmoorestech_masterリポジトリ内のPNGファイルを見つけてJPEGに変換し、アセット画像フォーマットを統一する。Use when: (1) moorestech_masterにPNG画像が混在している時 (2) 「PNGをJPEGに変換して」「画像フォーマットを統一して」と依頼された時 (3) 新しいアセット画像を追加した後にフォーマットを揃えたい時
creating-server-tests
by moorestechmoorestech_serverのNUnitテストを作成するスキル。テストの雛形生成、初期化パターン、命名規約、 テスト用IDの使い方を含む。 Use when: (1) moorestech_serverに新しいテストクラスを追加する時 (2) 「テストを書いて」「テストを作成して」とサーバー側のテスト作成を依頼された時 (3) 既存機能のテストカバレッジを追加する時 (4) CombinedTest/UnitTest/PacketTestのいずれかを作成する時
edit-schema
by moorestechマスターデータのYAMLスキーマを編集するためのガイド。スキーマの追加・変更・削除を行う際に使用する。 Use when:1.VanillaSchemaのymlファイル(blocks.yml,items.yml等)を編集する必要がある時2.新しいブロックタイプやパラメータを追加する 3.既存スキーマの構造を変更する4.SourceGeneratorのトリガー方法を確認する
recalc-research-sort-priority
by moorestechmoorestech mod の items.json/blocks.json の sortPriority を、research.json の解放順序と nodeGraph.v1.json 上のアイテム配置から再計算し、配列も sortPriority 順に並べ替える。Use When — 「sortPriority を再計算して」「アイテム並び順を整えて」「研究解放順でアイテム並べて」「v7/v8 mod のソート優先度を更新」と言われた場合。
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.