| name | three-tomato |
| description | 将需求文档转换为多端原生开发代码，支持 Android、iOS、鸿蒙、小程序、快应用、H5、Web、macOS、Windows 等 17 个平台。原生优先，AI 友好技术栈。 |
| license | MIT |
Three-Tomato
将需求文档转换为多端原生开发代码，支持 Android、iOS、鸿蒙、小程序、快应用、H5、Web、macOS、Windows 等 17 个平台。
核心原则：原生优先，AI 友好技术栈（数据集大、文档丰富）
Overview
This skill enables AI Agents to analyze requirement documents and generate native platform-specific code using AI-friendly tech stacks with large training datasets.
Supported Platforms
| Platform | Code | Language/Framework | AI-Friendly Reason |
|---|
| 原生移动端 | | | |
| Android | android | Kotlin + Jetpack Compose | Kotlin 数据集大，Compose 声明式 UI |
| iOS | ios | Swift + SwiftUI | Swift 现代语法，SwiftUI 声明式 |
| HarmonyOS | harmony | ArkTS + ArkUI | TypeScript 超集，AI 熟悉 |
| 小程序 | | | |
| WeChat Mini Program | wechat-mp | TypeScript + 原生 WXML | TS 类型安全，文档丰富 |
| Alipay Mini Program | alipay-mp | TypeScript + 原生 AXML | 同上 |
| Baidu Mini Program | baidu-mp | TypeScript + 原生 Swan | 同上 |
| Quick App | quick-app | TypeScript | 类 Vue 语法 |
| Web | | | |
| H5 Mobile | h5 | TypeScript + React | React 优先，数据集最大 |
| Web Desktop | web | TypeScript + React + Next.js | SSR/SSG，企业级首选 |
| 桌面原生 | | | |
| macOS | macos | Swift + SwiftUI | 原生 Apple 生态 |
| Windows | windows | C# + WinUI 3 | .NET 生态成熟，AI 友好 |
| 跨端移动 | | | |
| Flutter | flutter | Dart + Riverpod | 高性能，声明式 UI |
| React Native | react-native | TypeScript + Zustand | React 生态，AI 友好 |
| Uni-app | uni-app | Vue 3 + TypeScript | 一套代码多端运行 |
| Taro | taro | React + TypeScript | 京东出品，React 生态 |
| 跨端桌面 | | | |
| Electron | electron | React + TypeScript | Web 技术栈，成熟稳定 |
| Tauri | tauri | Rust + React | 高性能，体积小 |
Trigger Commands
Code Generation
🤖 "transform to [platform]"
🤖 "generate [platform] code"
🤖 "convert requirement to multi-platform"
🤖 "create platform specs"
🤖 "转换为[平台]代码"
🤖 "生成多端代码"
Self-Evolution
🧬 "3T evolve" / "3T 总结归纳进化"
🧬 "3T log issue [description]" / "3T 记录问题 [描述]"
🧬 "3T show evolution log" / "3T 查看进化日志"
Workflow
Evolution Workflow (3T evolve / 3T 总结归纳进化)
When 3T evolve command is triggered, AI Agent executes:
Step 1: Collect Feedback
- Scan
.three-tomato/feedback/issues/ for issue records
- Analyze compile/runtime errors in
.three-tomato/output/
- Identify patterns from user's manual code fixes
Step 2: Categorize Issues
- Group by platform (Android/iOS/Mini Programs, etc.)
- Group by type (syntax error/API changes/best practices/architecture)
- Calculate issue frequency
Step 3: Update Knowledge
- Update
references/patterns/known-issues.yaml - known issues database
- Update
references/patterns/best-practices.yaml - best practices
- Update platform-specific
plugins/[platform]-generator/PLUGIN.md
- Log changes to
.three-tomato/evolution/changelog.md
Step 4: Validate Improvements
- Generate test cases for high-frequency issues
- Verify improved templates resolve the issues
- Output evolution report to
.three-tomato/evolution/reports/
Phase 1: Requirement Analysis
- Read requirement document from
.three-tomato/requirements/ or specified path
- Parse and extract:
- Feature specifications
- UI/UX requirements
- Data models
- API interfaces
- Business logic flows
Phase 2: Platform Selection
- Read
.three-tomato/config.yaml for target platforms
- Check enabled plugins in
plugins/_registry.yaml
- Determine output platforms based on user command or config
Phase 3: Code Generation
For each target platform:
- Load platform-specific templates from
references/templates/[platform]/
- Apply tech stack configurations
- Generate:
- Project structure
- UI components
- Data models
- API clients
- Business logic
- Platform-specific configurations
Phase 4: Output & Validation
- Write generated code to
.three-tomato/output/[platform]/
- Generate comparison matrix
- Create migration guides between platforms
Configuration
Read configuration from .three-tomato/config.yaml:
platforms:
- android
- ios
- wechat-mp
- h5
tech_stack:
android:
language: kotlin
ui: compose
architecture: mvvm
di: hilt
network: retrofit + okhttp
async: coroutines
ios:
language: swift
ui: swiftui
architecture: mvvm
network: urlsession
async: async-await
harmony:
language: arkts
ui: arkui
architecture: mvvm
wechat-mp:
language: typescript
Plugin System
Hook Points
Plugins can extend functionality at these hooks:
before_analyze - Pre-process requirement documentsafter_analyze - Post-process extracted requirementsbefore_generate - Modify generation parameterson_generate - Custom code generation logicafter_generate - Post-process generated codeon_export - Custom export formats
Plugin Commands
📋 "list platform plugins"
📦 "install plugin <source>"
✅ "enable plugin <name>"
❌ "disable plugin <name>"
Built-in Plugins
| Plugin | Description |
|---|
| 原生平台 | |
android-generator | Android 原生 (Kotlin + Compose) |
ios-generator | iOS 原生 (Swift + SwiftUI) |
harmony-generator | HarmonyOS (ArkTS + ArkUI) |
wechat-mp-generator | 微信小程序 (TypeScript) |
alipay-mp-generator | 支付宝小程序 (TypeScript) |
baidu-mp-generator | 百度智能小程序 (TypeScript) |
quick-app-generator | 快应用 (TypeScript) |
h5-generator | H5 移动端 (React + TypeScript) |
web-generator | Web 桌面端 (Next.js + React) |
macos-generator | macOS 原生 (Swift + SwiftUI) |
windows-generator | Windows 原生 (C# + WinUI 3) |
| 跨端框架 | |
flutter-generator | Flutter (Dart + Riverpod) |
rn-generator | React Native (TypeScript + Zustand) |
uni-app-generator | Uni-app (Vue 3 + TypeScript) |
taro-generator | Taro (React + TypeScript) |
electron-generator | Electron (React + TypeScript) |
tauri-generator | Tauri (Rust + React) |
| 工具类 | |
api-sync | 跨平台 API 定义同步 |
ui-converter | UI 组件跨平台转换 |
i18n-sync | 国际化资源同步 |
diff-report | 平台差异报告生成 |
Output Structure
.three-tomato/
├── config.yaml # Main configuration
├── requirements/ # Input requirement documents
│ ├── PRD.md # Product requirement document
│ ├── api.yaml # API specifications
│ └── ui-specs/ # UI design specs
├── cache/ # Incremental cache
├── output/ # Generated code
│ ├── android/ # Kotlin + Compose
│ ├── ios/ # Swift + SwiftUI
│ ├── harmony/ # ArkTS + ArkUI
│ ├── wechat-mp/ # TypeScript 原生
│ ├── alipay-mp/ # TypeScript 原生
│ ├── baidu-mp/ # TypeScript 原生
│ ├── quick-app/ # TypeScript
│ ├── h5/ # React + TypeScript
│ ├── web/ # Next.js + React
│ ├── macos/ # Swift + SwiftUI
│ ├── windows/ # C# + WinUI 3
│ └── _shared/ # 共享资源
│ ├── models/ # 数据模型定义
│ ├── api/ # API 接口定义
│ └── assets/ # 共享资源文件
├── reports/ # Analysis reports
│ ├── comparison.md # Platform comparison
│ ├── migration.md # Migration guide
│ └── compatibility.md # Compatibility matrix
└── i18n/ # Multi-language docs
├── en/
└── zh/
Important Instructions
For AI Agent:
- Always read config first: Check
.three-tomato/config.yaml before generation
- Respect tech stack: Use configured frameworks and libraries
- Maintain consistency: Ensure data models and API calls are consistent across platforms
- Platform idioms: Follow each platform's best practices and conventions
- Protocol Retention:
- If requirement/legacy uses gRPC:
- Supported Platforms (iOS, Android, Windows, macOS, Flutter): MUST use gRPC to maintain high performance and type safety.
- Restricted Platforms (Mini Programs, H5): Fallback to HTTP/JSON (via Envoy/Gateway).
- If requirement uses REST/GraphQL: Use standard HTTP clients.
- Incremental updates: Only regenerate changed parts using cache
- Preserve user content: Content marked with
<!-- user-content --> must not be overwritten
Code Quality Standards:
- Follow platform-specific coding guidelines
- Include proper error handling
- Add necessary comments (in configured language)
- Generate unit tests when
include_tests: true
- Create platform-specific README with setup instructions
Cross-Platform Consistency:
- Unified data model definitions
- Consistent API interface naming
- Shared business logic documentation
- Synchronized i18n resources
References
- Templates:
references/templates/
- Prompts:
references/prompts/
- Examples:
references/examples/
- Platform Guides:
docs/platforms/
Version
- Skill Version: 1.0.1
- Last Updated: 2026-01-30
- Author: three-tomato
