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...
argus-project
by Djude1Argus 專案專用工作規則。當 Codex 在 D:\GitHub_Project\Argus 工作、使用者提到 Argus、網站健檢 SaaS、SEO/AEO/GEO/security 掃描、Playwright 爬蟲、Django/React/Celery、LLM Agent、API Key/model 選擇、MiniMax/GLM/Gemini、RTK、交接存檔或專案參考資料時，必須使用此 skill；開始實作前必須讀取本 skill 與其 references。
argus-git-safety
by Djude1Argus 版本控制 / commit / push / 協作安全規範與部署現況。當你要在本 repo 做 git add / commit / push、或討論部署、上線、共用 repo、與組員協作時，**動手前必讀並照做**。內含：專案已公網上線、GitHub 與部署機共用、有其他組員同時開發，以及任何 push 前的強制檢查清單。
argus-ui-design
by Djude1Argus 前端 UI/UX 設計與實作準則（科技風 / 前台動效 / 前後台對應 / affordance / 返回導覽）。當你要新增或修改 Argus 任何前端介面時必讀——包含 frontend/src/App.jsx 的頁面與元件、styles.css 樣式、前台公開頁（public-shell）、React 後台（/admin/*）、新頁面或新元件、按鈕 / 導覽列 / 分頁 / 動畫 / 特效 / 配色 / 版面 / 互動、或任何「美化、調整版面、做動畫、改視覺」的需求時，先讀本 skill 再動手。
scope-and-environment-check
by Djude1Argus 專案「範圍與環境感知」強制規則。**必須主動呼叫**的情境：(a) 對話開始或接到新任務時的第一步；(b) 使用者問題含「整個 / 所有 / 每個 / 全部 / 列出 / 介紹專案」這類**全稱詞**，回答前先做；(c) 使用者糾錯、質疑、補充事實後（不論大小）；(d) 在 worktree 工作但要回答主 repo 全貌時；(e) `ls` / `find` / `grep` 後要做「總結性 / 全面性」回答前。核心鐵律：**先宣告檢查範圍與已知盲區，再回答內容**；視野有限就主動承認；使用者糾錯一次 → 同類型回頭掃一遍 → 修補規則本身。N+1 不同方法測試的循環細節見 CLAUDE.md「QA 鐵則」。
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.