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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wandb
by botterYosukeFlow Surface × Weights & Biases 統合ガイド。戦略実験のナラティブ記録・マルチエージェント比較・Sweep によるハイパーパラメータ探索の実装パターンを定義する。コア非汚染ルールと examples/wandb/ への閉じ込め方針も含む。
e-station-review
by botterYosukee-station の実装レビュー用スキル。plan/spec/architecture/open questions の整合、phase/task/deferred の境界、Rust-Python 間の契約、bootstrap/reconnect/recovery の見落としや silent failure を優先して洗う。新規テストの収集不能、イベント順序の取りこぼし、pending replay、ready cache、UI 文言契約の崩れも重点確認する。
tachibana-e-api
by botterYosuke立花証券 e支店 API（v4r7/v4r8、tachibana）でコードを書く・運用するときの必読スキル。「立花」「e支店」「ｅ支店」「tachibana」「kabuka.e-shiten.jp」「demo-kabuka」「sUrlRequest」「sUrlEvent」「CLMAuthLoginRequest」「CLMKabuNewOrder」「sCLMID」「sResultCode」「p_errno」「sJsonOfmt」に触れたら必ず起動する。CLMAuthLoginRequest によるログイン、仮想 URL（sUrlRequest/Master/Price/Event/EventWebSocket）の取り扱い、`{virtual_url}?{JSON文字列}` 独自形式、p_no/p_sd_date/sJsonOfmt の必須化、p_errno と sResultCode の二段判定、Shift-JIS、空配列が "" で返る件、第二暗証番号の必須化、CLMKabuNewOrder のパラメータ、EVENT/WebSocket の ^A^B^C 区切り、CLMEventDownload マスタの特殊フロー、flowsurface ローカル起動時の debug/release・.env・セッションキャッシュ・ポート衝突の落とし穴を扱う。
nautilus-trader
by botterYosukeAuthoritative development helper for the **nautilus_trader** framework — the core engine of this project (The-Trader-Was-Replaced). Use this skill whenever the user is working with nautilus_trader APIs, even if they don't name it explicitly: Actors, Strategies, the message bus, the data engine, clocks/timers, bar/quote/trade data types, instruments, `BacktestEngine` / `BacktestNode` / `BacktestEngineConfig`, `TradingNode` / `LiveExecEngine`, `NautilusKernel`, ParquetDataCatalog, indicators, custom data, adapters, or anything in `python/engine/nautilus_*.py`. Also trigger on the **precision-mode / catalog-parquet-schema seam** (GH #34): "HIGH_PRECISION", "PRECISION_BYTES", "FIXED_PRECISION", "standard vs high precision", "8-byte / 16-byte", "i64 / i128", "fixed_size_binary", "PrecisionMismatch", "precision mismatch", "catalog precision", `Price.from_raw` / `Quantity.from_raw` raw scaling, the on-disk catalog layout `data/<bar|trade_tick|quote_tick>/<identifier>/*.parquet`, `class_to_filename` / `filename_to_cl
grill-with-docs
by botterYosukeGrilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented decisions. **ユーザーが `gh issue #N を実施してください /plan /grill-with-docs` のようにコマンドリストに `/grill-with-docs` を含めたときは必ず発動する**（#199 実例: `/plan /grill-with-docs /diagnose` が羅列されていたのに tdd と diagnose の確認に注力して grill-with-docs を発動しなかった）。複数スキルが羅列されたコマンドでも各スキルを順番に invoke すること。 **実装後の docs/wiki 整合確認にも使う**: 「実装した内容が wiki と食い違っていないか確認したい」「API の呼び出し側の挙動を docs と照合したい」「契約を明文化する前に docs を読みたい」といった場面でも起動する。実装に入る前の設計ドリルだけでなく、**実装後に呼び出し側コード（`_backend_impl.py` の `hasattr` dispatch など）と docs/wiki のどちらが正しいか確認するコードリーディングとしても機能**する（実例: #189 で `set_execution_hooks` の呼び出しパターンを `_backend_impl.py` で確認）。
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.