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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zenmux-statusline
by ZenMuxInstall and configure a Claude Code status line that displays real-time ZenMux account information: subscription tier, 5-hour and 7-day quota usage with color-coded progress bars, and PAYG wallet balance, alongside standard session info (model, git, context usage, prompt cache). Trigger on: "status line", "statusline", "set up status bar", "show ZenMux in status bar", "install ZenMux statusline", "configure status line with ZenMux", "状态栏", "配置状态栏", "安装状态栏", "在状态栏显示ZenMux信息". Activate when user wants to SET UP, INSTALL, CONFIGURE, or CUSTOMIZE a Claude Code status line that includes ZenMux account data. Do NOT trigger for querying usage interactively (use zenmux-usage), docs (use zenmux-context), or general setup (use zenmux-setup).
zenmux-image-generation
by ZenMuxGenerate or edit images through ZenMux image models such as openai/gpt-image-2, Nano Banana Pro / Gemini 3 Pro Image, Nano Banana 2, Qwen Image, Doubao Seedream, ERNIE-Image, GLM-Image, Hunyuan Image, KlingAI Kling, and future ZenMux image models. Use for text-to-image, image editing from references or URLs, photos, portraits, logos, product shots, posters, infographics, comics, ads, UI mockups, marketing creatives, packaging mocks, diagrams, characters, style transfer, virtual try-on, and other visual assets. Trigger on create, generate, render, design, draw, paint, edit, remix, 生成图片, 画一张, 出图, AI 画图, 文生图, 图生图, 设计海报, 做 logo, 改图, P 图, 图片编辑, 帮我画, 用 ZenMux 生图. In a ZenMux project context, prefer this skill for image output.
optimize-chinese-docs
by ZenMuxOptimizes and polishes Chinese ZenMux product documentation. Use this skill whenever the user wants to refine, polish, optimize, or improve Chinese markdown documentation — including drafts, existing docs, or files that need professional enhancement. Trigger on phrases like "润色", "优化", "改进文档", "refine docs", "polish docs", "improve documentation quality", or when the user provides a draft and asks for improvement. Also use when the user runs `pnpm run optimize` or asks to apply documentation standards to existing content.
translate-docs
by ZenMuxTranslates Chinese ZenMux documentation into professional English. Use this skill whenever the user wants to translate files under docs_source/zh/ to English, mentions /translate, asks to sync Chinese docs to English, or wants to create or update English versions of any doc in the ZenMux docs repo. Handles single files, multiple files, or entire directories, then automatically verifies (and fixes) sidebar entries in both config files.
morphe
by ZenMuxBuild and deploy a Next.js, Bigfish (@alipay/bigfish), or Vite project to the Morphe service (https://morphe.zenmux.app), targeting a linux-x64-gnu runtime. Use when the user asks to deploy, ship, publish, or release a Next.js, Bigfish, or Vite app to Morphe, run "morphe deploy", or otherwise push a build to the Morphe / zenmux platform. Handles login, framework detection, Next.js standalone validation / config fixing, Bigfish static-server wrapping, Vite SPA static wrapping or custom-server (server.ts/js) esbuild bundling, building, zipping, OSS upload, CRC64 checksum, .morphe.json management, and the deploy API call.
zenmux-usage
by ZenMuxQuery real-time ZenMux account data via the Management API: subscription detail, account status, quota usage (5h/7d/monthly), Flow rate, PAYG balance, per-generation cost/tokens, timeseries trends, model leaderboards, and provider market share. Use when the user wants current usage, remaining quota, credits, balance, bonus credits, Flow rate, generation cost, token/cost trends, top models, rankings, or provider share. Trigger on "check my usage", "quota left", "my balance", "subscription status", "Flow rate", "generation cost", "usage trend", "top models", "leaderboard", "market share", "provider breakdown", "查用量", "余额", "配额", "订阅详情", "Flow 汇率", "额度还剩多少", "使用趋势", "排行榜", "模型排名", "供应商占比". Do not use for docs, setup, top-up, troubleshooting, or code-writing.
zenmux-codex-pets
by ZenMuxInstall bundled ZenMux Codex APP pets from this skill into the user's Codex pets directory. Use when the user asks to install, copy, enable, set up, or use ZenMux pets, Codex pets, Codex APP pets, pet.json pet folders, or the bundled pets under zenmux-codex-pets/pets. Trigger on "install ZenMux pets", "Codex pets", "安装 ZenMux pets", "安装 Codex 宠物", "复制 pets 到 .codex/pets".
zenmux-setup
by ZenMuxGuide users through configuring ZenMux Base URL, API endpoint, API Key, and model settings for tools or SDKs. Use when the user wants to set up, configure, or connect ZenMux in Cursor, Claude Code, Cline, Cherry Studio, Open-WebUI, Dify, Obsidian, Sider, Copilot, Codex, Gemini CLI, opencode, or custom SDK code. Trigger on "configure", "setup", "set up", "base url", "endpoint", "api key", "how do I set up ZenMux", "help me fill in API settings", "接入", "配置", "设置", "base url 填什么", "怎么填", "怎么接入", "API 地址", "接口地址". Treat the user as a first-time user and guide step by step. Do not use for usage queries or docs lookups; use zenmux-usage or zenmux-context instead.
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.