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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youtube-content-gen
by kennyzirA complete pipeline to convert YouTube videos into high-quality, SEO-optimized guide pages using Gemini AI.
youtube-intel
by kennyzirYouTube内容情报与竞品监测。当用户需要分析YouTube频道、追踪竞品动态、发现内容机会时触发。功能：1) Monitoring - 监测指定频道的更新频率、内容方向、数据表现；2) Discovery - 输入类目/关键词，扫描市场机会与竞争程度。用于选题策划、竞品分析、内容策略制定。
youtube-transcribe
by kennyzirYouTube video transcription and memory workflow. Triggers when user shares a YouTube URL and asks to transcribe, get transcript, extract content, "转录", "transcribe this video". Downloads audio via yt-dlp (android client to avoid 403, with web fallback), converts with ffmpeg, transcribes with whisper CLI, then saves full transcript + summary to today's memory file.
youtube-game-keywords
by kennyzirYouTube 订阅频道游戏关键词提取工具。自动抓取订阅频道视频，识别游戏关键词， 归类为 Roblox / 独立游戏 / 主机PC 等赛道，生成结构化日报。 触发词：YouTube 游戏关键词日报 / YouTube 订阅源 / 游戏热度 / YouTube game keywords。
x-demand-radar
by kennyzirAI 热点雷达 - 在新 AI 工具/关键词爆火前发现它们，抢注域名、建站套利。 触发条件： - 用户说「跑雷达」、「热点扫描」、「X 雷达」 - cron 每 12 小时自动触发（9:00, 21:00） 核心目标： 在 AI 关键词/工具首次病毒传播 → 大众认知的 24-72h 窗口内发现， 抢注域名 + 建工具站 + 吃搜索流量红利。 典型案例：Nano Banana、Ghibli AI、OpenClaw、Hermes
roblox-game-data-scraper
by kennyzir自动化抓取 Roblox 游戏数据的完整工具链。支持从 Trello、Discord、Reddit 和游戏内 API 收集代码、物品、角色、交易价值等结构化数据。
roblox-site-architect
by kennyzir专用于构建高流量 Roblox 游戏工具站的 SEO 架构与工程化方法论。从给定游戏词到最终上线部署的完整流程，包含每日自动关键词挖掘→页面构建→部署管道。
rpg-stat-catalyst
by kennyzirA core TypeScript library for RPG game mathematics. Handles attribute points, leveling curves, and stat derivation (HP, Dmg, etc.).
gemini-thinking-protocol
by kennyzir核心认知引擎 - 强制执行 First Principles, Dialectics, Systems Thinking 与 Communication Protocol
auto-page-sync
by kennyzir仓库 Markdown/JSON 内容自动同步到前端页面，配置 GitHub Actions 定时拉取 + 部署，保持 Google SEO 内容新鲜度。支持日报、博客、Changelog、Landing Page 等多种页面模式。
backlink-discovery
by kennyzir外链机会发现引擎。用户输入目标网址，立即用 web_search 开始多轮关键词派生搜索， 目标发现 300 个相关平台后自动停止，结果存入该网址专属数据库。 不限外链方式（论坛/GitHub/Wiki/目录等），只发现不执行。 触发：用户提供网址，说"发现外链"、"找外链机会"、"发现外链机会"。
backlink-intelligence
by kennyzirAI/Tools目录外链情报收集与评估。当需要为项目寻找外链机会时使用，包括： (1) 搜索免费AI工具目录、SaaS目录、创业目录 (2) 批量评估目标站点的收费/登录/审核要求 (3) 记录外链提交结果到memory文件 (4) 生成待提交清单 触发场景：用户要求"提交外链"、"找目录"、"增加曝光"、"外链情报"等
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.