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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skillify
by ZhangHanDongCapture this session's repeatable process into a reusable skill. Use when: user says "make this a skill", "capture this workflow", "skillify", "save this as a skill", "turn this into a skill", "create skill from session", "把这个流程变成技能", "提炼成skill", "保存为技能"
makepad-2-0-migration
by ZhangHanDongCRITICAL: Use for migrating from Makepad 1.x to 2.0. Triggers on: makepad migration, live_design to script_mod, makepad upgrade, makepad 1.x, old syntax, new syntax, makepad breaking changes, makepad 迁移, 旧语法, LiveHook to ScriptHook, apply_over to script_apply_eval, Live to Script, live_design!, angle brackets to curly braces
makepad-2-0-layout
by ZhangHanDongCRITICAL: Use for Makepad 2.0 layout system. Triggers on: makepad layout, makepad width, makepad height, makepad flex, makepad flow, makepad padding, makepad margin, makepad spacing, makepad align, makepad sizing, Fill, Fit, Inset, Flow.Down, Flow.Right, ScrollXView, ScrollYView, 布局, 对齐, 间距, 填充, 排版, 滚动视图, 尺寸, 宽度, 高度
makepad-2-0-events
by ZhangHanDongCRITICAL: Use for Makepad 2.0 event and action handling. Triggers on: makepad event, makepad action, MatchEvent, handle_event, handle_actions, on_click, on_render, on_return, on_startup, script_eval!, script_apply_eval!, button clicked, text changed, slider changed, checkbox toggled, Hit, FingerDown, FingerUp, KeyDown, KeyUp, Focus, ids!, TextCopy, TextCut, SelectionHandleDrag, PopupDismissed, clipboard, selection, IME, ImeAction, popup window events, video inputs, camera events, 事件, 动作, 点击, 输入, 回调, 交互, 事件处理, 剪贴板, 选择, 弹出窗口
makepad-2-0-dsl
by ZhangHanDongCRITICAL: Use for Makepad 2.0 DSL syntax and property system. Triggers on: makepad dsl, script_mod!, makepad syntax, makepad property, makepad 2.0 syntax, colon syntax, merge operator, named instance, let binding, mod.widgets, register_widget, script_component, type_default, widgets_internal
makepad-2-0-design-judgment
by ZhangHanDongCRITICAL: Entry-level skill for Makepad 2.0 GUI development. This is the FIRST skill to load for any Makepad task — it provides design judgment anchors ABOVE the other 13 Makepad 2.0 skills. Triggers on: makepad, makepad app, makepad project, makepad design, live_design!, app_main!, script_mod!, Cx, WidgetRef, Widget, makepad-widgets, makepad architecture, makepad how to, "how should I", "should I use", "what's the best way", makepad 架构, makepad 设计, makepad 怎么做, makepad 最佳实践, 组件拆分, 状态管理, 数据流, 渲染思维
makepad-2-0-app-structure
by ZhangHanDongCRITICAL: Use for Makepad 2.0 app structure and Rust integration. Triggers on: makepad app, makepad getting started, app_main!, App::run, MatchEvent, AppMain, handle_event, handle_actions, ScriptVm, from_script_mod, makepad boilerplate, makepad new project, makepad cargo, Cargo.toml setup, hot reload, --hot, live reload, wasm deploy, cargo makepad, media plugin, audio_output, audio_input, AudioBuffer, cx.audio, makepad audio, 音频, 应用结构, 入门, 新项目, 脚手架, 启动, 热重载, 部署
makepad-2-0-animation
by ZhangHanDongCRITICAL: Use for Makepad 2.0 animation system. Triggers on: makepad animation, makepad animator, Animator, AnimatorState, hover effect, makepad transition, animation state, Forward, Snap, Loop, ease function, makepad animate, timeline, snap(), default @off, animation group, 动画, 过渡, 悬停效果, 动画状态, 缓动函数
makepad-2-0-performance
by ZhangHanDongCRITICAL: Use for Makepad 2.0 performance optimization and debugging. Triggers on: makepad performance, makepad debug, makepad profiling, makepad gc, new_batch, texture_caching, render optimization, draw batching, mod.gc, garbage collection, memory, debug logging, troubleshoot, ViewOptimize, PortalList, CachedView, render tree, invisible text, text disappears, UI freezes, scroll stuttering, 性能, 调试, 优化, 垃圾回收, 渲染, 批处理, 日志
makepad-2-0-troubleshooting
by ZhangHanDongCRITICAL: Use for Makepad 2.0 troubleshooting and common mistakes. Triggers on: makepad error, makepad bug, makepad problem, makepad issue, makepad not working, text invisible, widget not showing, click not working, height zero, makepad pitfall, makepad gotcha, makepad FAQ, makepad help, script_mod error, compile error, widget not found, render not updating, hot reload not working, wasm build error, port conflict, server lock, IME popup, selection handle, popup window crash, canvas splash, POST splash loop, 100% CPU, set_visible not working, on_render empty, event bridge unreliable, float time display, fn tick not called, on_audio not called, button click through, 常见错误, 问题排查, 故障排除, 不显示, 不工作, 看不见, 热重载, 编译错误
makepad-2-0-theme
by ZhangHanDongCRITICAL: Use for Makepad 2.0 theme system. Triggers on: makepad theme, theme variable, theme color, theme font, theme spacing, dark mode, light mode, theme switching, mod.themes, theme_mod, theme.color_, theme.font_, theme.space_, theme.mspace_, 主题, 颜色, 字体, 暗色模式, 亮色模式, 主题切换, 样式
makepad-2-0-splash
by ZhangHanDongCRITICAL: Use for Makepad 2.0 Splash scripting language. Triggers on: splash language, makepad script, script_mod!, makepad scripting, splash 脚本, makepad 2.0 script, mod.state, on_render, script_eval, streaming evaluation, splash syntax, splash vm, let binding, splash functions, hot reload, live reload, ScriptModKey, script_mod_overrides, checkpoint, incremental parsing, canvas splash, POST splash, fn tick, on_audio, set_text, tab switching, 音乐播放器, token monitor, driver script, audio API, 热重载, 脚本引擎, 增量解析
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.