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...
eob1-cht
by wicanr2接續 1991 Westwood/SSI 魔眼殺機 1 (Eye of the Beholder 1, EOB1) 繁體中文化 reverse engineering 與 patch 工作。當使用者提到 EOB1、魔眼殺機 1、Eye of the Beholder 1、EOB.EXE 字串翻譯、MCGA.OVL 字型 hook、Big5/cp950 in-place patch、CHINFONT.FNT 16x14 glyph、想翻譯 CAMP menu (休息隊伍/記憶法術)、角色生成 (戰士/守序善良)、咒語、戰鬥訊息、想加 16x14 真中文取代 8x8 byte-pair 中文，或處理同類 1991 年 Westwood DOS RPG (OVL+EXE 雙層架構) 字型 hook + 字串 in-place patch 時觸發。也涵蓋同類技術: x86 16-bit MZ overlay prefix-hook、Big5 endianness 對齊 (subset BE-stored / x86 LE-read)、stateful per-call Big5 detection (lead saves / trail renders)、const patch 把執行期 scratch buffer 推離 hook 區域、binary search 字模 subset 格式。**主動觸發**: 即使使用者只說「繼續做 EOB1」或「擴充翻譯」也要套用此 skill。
wing-portable-sfx
by wicanr2接續 1994-1996 年 WinG-based Win9x 遊戲在現代 Windows 10/11 上的相容性處理 + 包成單檔 portable .exe 的工作。當使用者提到 WinG / WING32.DLL、「WinG Installation Error」對話框、Panzer General / SimCity 2000 / Civilization II / Master of Magic II / Lode Runner 等 1990s WinG 遊戲在 Win10/11 跑不動或畫面花掉、字型在高 DPI 螢幕變粗/糊(同 EXE 換資料夾粗細不一)、要切換 256 色相容模式、想加 GDIDPISCALING/DPIUNAWARE 旗標、想擺脫 HKCU AppCompat registry shim、把舊遊戲整包成單一可雙擊 portable .exe (放隨身碟)、7-Zip SFX (7z.sfx) 自製、icon stamping、UTF-8 BOM 編碼問題時都觸發。也涵蓋同類 Win9x 16-bit thunk-failed PE binary patch、只用系統內建工具 (7z + PowerShell P/Invoke) 不裝任何第三方 tool 製作 self-extractor 的技術。
retro-game-remake
by wicanr2把 1980s–90s 老遊戲(尤其 CRPG)逆向工程 + 乾淨重寫成跨平台 C/SDL2 + 繁中化的階層式方法論(反編當 oracle 不照抄)。觸發:「重製/移植/中文化老遊戲」「反組譯遊戲執行檔」「破解老遊戲資料格式」「抽 FM Towns/DOS 美術或音樂」「把老遊戲做成跨平台可玩」「u2-cht/u3-cht/u6-cht/opendw」。完整策略與七階段見內文 + references/。
esc-cancel-f10-quit-autosave
by wicanr2互動式 app (GUI/TUI/遊戲) 離開語意鐵則:ESC 只 cancel/back,F10(或 Ctrl+Q)才離開,離開前跳 Yes/No 並自動存檔。觸發:做 input handler/選單導航/quit 鍵/存檔系統,或「按 ESC 跳出/結束」「按鍵設計」「離開前確認」「自動存檔」「quit dialog」「modal」「按錯鍵丟進度」。SDL/curses/web/Electron/CLI wizard 皆適用。完整鐵則見內文。
qb64pe-game-linux-port
by wicanr2用 QB64-PE + Docker 把 QuickBasic/.bas 遊戲(尤其中文化版)cross-compile 成 Linux ELF/AppImage,並 Wine 跨編 Windows .exe。內建 QB64-PE on Linux 兩大必踩雷 + 中文點陣字/自動存檔/作弊/UI 優化 modular patch pipeline。觸發:「.bas/.qb/QuickBasic/QB64 遊戲跑在 Linux/Windows」「包 AppImage」「Windows 跨編譯」「中文字型升級」「加 cheat/自動存檔」。完整 patch 清單見內文。
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.