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android-clean-architecture

Android 與 Kotlin Multiplatform 專案的 Clean Architecture 模式 — 涵蓋模組結構、依賴規則、UseCase、Repository 及資料層模式。在組織模組、層級或資料流程時使用。

android-clean-architecture 是什麼?

這個技能提供了適用於 Android 與 Kotlin Multiplatform (KMP) 專案的完整 Clean Architecture 實作指南。它明確定義了模組邊界、依賴反轉原則,以及 UseCase/Repository 模式,確保領域層完全獨立於資料層與表示層。技能涵蓋了使用 Room (Android)、SQLDelight (KMP) 和 Ktor 的資料層設計,並包含使用 Koin 或 Hilt 的依賴注入設定範例。透過遵循此架構,開發者可以建立可測試、可維護且與框架無關的程式碼庫,避免常見的反模式,如將業務邏輯放入 ViewModel 或產生循環模組依賴。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/android-clean-architecture

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說明文件

Android Clean Architecture

Android 與 KMP 專案的 Clean Architecture 模式。涵蓋模組邊界、依賴反轉、UseCase/Repository 模式,以及使用 Room、SQLDelight 和 Ktor 的資料層設計。

何時啟用

  • 組織 Android 或 KMP 專案模組
  • 實作 UseCase、Repository 或 DataSource
  • 設計各層(領域層、資料層、表示層)之間的資料流程
  • 使用 Koin 或 Hilt 設定依賴注入
  • 在分層架構中使用 Room、SQLDelight 或 Ktor

模組結構

建議佈局

project/
├── app/                  # Android 入口點、DI 接線、Application 類別
├── core/                 # 共用工具、基礎類別、錯誤類型
├── domain/               # UseCase、領域模型、Repository 介面(純 Kotlin)
├── data/                 # Repository 實作、DataSource、資料庫、網路
├── presentation/         # 畫面、ViewModel、UI 模型、導航
├── design-system/        # 可複用的 Compose 元件、主題、排版
└── feature/              # 功能模組(選擇性,適用於大型專案)
    ├── auth/
    ├── settings/
    └── profile/

依賴規則

app → presentation, domain, data, core
presentation → domain, design-system, core
data → domain, core
domain → core(或無依賴)
core → (無)

關鍵:domain 絕不能依賴 data、presentation 或任何框架。它只包含純 Kotlin。

領域層

UseCase 模式

每個 UseCase 代表一個商業操作。使用 operator fun invoke 保持清晰的呼叫方式:

class GetItemsByCategoryUseCase(
    private val repository: ItemRepository
) {
    suspend operator fun invoke(category: String): Result<List<Item>> {
        return repository.getItemsByCategory(category)
    }
}

// 基於 Flow 的 UseCase,用於反應式串流
class ObserveUserProgressUseCase(
    private val repository: UserRepository
) {
    operator fun invoke(userId: String): Flow<UserProgress> {
        return repository.observeProgress(userId)
    }
}

領域模型

領域模型是純 plain Kotlin data class — 不包含框架註解:

data class Item(
    val id: String,
    val title: String,
    val description: String,
    val tags: List<String>,
    val status: Status,
    val category: String
)

enum class Status { DRAFT, ACTIVE, ARCHIVED }

Repository 介面

定義在領域層,實作在資料層:

interface ItemRepository {
    suspend fun getItemsByCategory(category: String): Result<List<Item>>
    suspend fun saveItem(item: Item): Result<Unit>
    fun observeItems(): Flow<List<Item>>
}

資料層

Repository 實作

在本地與遠端資料來源之間協調:

class ItemRepositoryImpl(
    private val localDataSource: ItemLocalDataSource,
    private val remoteDataSource: ItemRemoteDataSource
) : ItemRepository {

    override suspend fun getItemsByCategory(category: String): Result<List<Item>> {
        return runCatching {
            val remote = remoteDataSource.fetchItems(category)
            localDataSource.insertItems(remote.map { it.toEntity() })
            localDataSource.getItemsByCategory(category).map { it.toDomain() }
        }
    }

    override suspend fun saveItem(item: Item): Result<Unit> {
        return runCatching {
            localDataSource.insertItems(listOf(item.toEntity()))
        }
    }

    override fun observeItems(): Flow<List<Item>> {
        return localDataSource.observeAll().map { entities ->
            entities.map { it.toDomain() }
        }
    }
}

Mapper 模式

將 mapper 作為資料模型附近擴充函式:

// 在資料層
fun ItemEntity.toDomain() = Item(
    id = id,
    title = title,
    description = description,
    tags = tags.split("|"),
    status = Status.valueOf(status),
    category = category
)

fun ItemDto.toEntity() = ItemEntity(
    id = id,
    title = title,
    description = description,
    tags = tags.joinToString("|"),
    status = status,
    category = category
)

Room 資料庫 (Android)

@Entity(tableName = "items")
data class ItemEntity(
    @PrimaryKey val id: String,
    val title: String,
    val description: String,
    val tags: String,
    val status: String,
    val category: String
)

@Dao
interface ItemDao {
    @Query("SELECT * FROM items WHERE category = :category")
    suspend fun getByCategory(category: String): List<ItemEntity>

    @Upsert
    suspend fun upsert(items: List<ItemEntity>)

    @Query("SELECT * FROM items")
    fun observeAll(): Flow<List<ItemEntity>>
}

SQLDelight (KMP)

-- Item.sq
CREATE TABLE ItemEntity (
    id TEXT NOT NULL PRIMARY KEY,
    title TEXT NOT NULL,
    description TEXT NOT NULL,
    tags TEXT NOT NULL,
    status TEXT NOT NULL,
    category TEXT NOT NULL
);

getByCategory:
SELECT * FROM ItemEntity WHERE category = ?;

upsert:
INSERT OR REPLACE INTO ItemEntity (id, title, description, tags, status, category)
VALUES (?, ?, ?, ?, ?, ?);

observeAll:
SELECT * FROM ItemEntity;

Ktor 網路客戶端 (KMP)

class ItemRemoteDataSource(private val client: HttpClient) {

    suspend fun fetchItems(category: String): List<ItemDto> {
        return client.get("api/items") {
            parameter("category", category)
        }.body()
    }
}

// HttpClient 設定與內容協商
val httpClient = HttpClient {
    install(ContentNegotiation) { json(Json { ignoreUnknownKeys = true }) }
    install(Logging) { level = LogLevel.HEADERS }
    defaultRequest { url("https://api.example.com/") }
}

依賴注入

Koin (KMP 相容)

// 領域模組
val domainModule = module {
    factory { GetItemsByCategoryUseCase(get()) }
    factory { ObserveUserProgressUseCase(get()) }
}

// 資料模組
val dataModule = module {
    single<ItemRepository> { ItemRepositoryImpl(get(), get()) }
    single { ItemLocalDataSource(get()) }
    single { ItemRemoteDataSource(get()) }
}

// 表示層模組
val presentationModule = module {
    viewModelOf(::ItemListViewModel)
    viewModelOf(::DashboardViewModel)
}

Hilt (僅限 Android)

@Module
@InstallIn(SingletonComponent::class)
abstract class RepositoryModule {
    @Binds
    abstract fun bindItemRepository(impl: ItemRepositoryImpl): ItemRepository
}

@HiltViewModel
class ItemListViewModel @Inject constructor(
    private val getItems: GetItemsByCategoryUseCase
) : ViewModel()

錯誤處理

Result/Try 模式

使用 Result<T> 或自訂密封類型進行錯誤傳遞:

sealed interface Try<out T> {
    data class Success<T>(val value: T) : Try<T>
    data class Failure(val error: AppError) : Try<Nothing>
}

sealed interface AppError {
    data class Network(val message: String) : AppError
    data class Database(val message: String) : AppError
    data object Unauthorized : AppError
}

// 在 ViewModel 中 — 映射到 UI 狀態
viewModelScope.launch {
    when (val result = getItems(category)) {
        is Try.Success -> _state.update { it.copy(items = result.value, isLoading = false) }
        is Try.Failure -> _state.update { it.copy(error = result.error.toMessage(), isLoading = false) }
    }
}

Convention 外掛 (Gradle)

對於 KMP 專案,使用 convention 外掛以減少建置檔案重複:

// build-logic/src/main/kotlin/kmp-library.gradle.kts
plugins {
    id("org.jetbrains.kotlin.multiplatform")
}

kotlin {
    androidTarget()
    iosX64(); iosArm64(); iosSimulatorArm64()
    sourceSets {
        commonMain.dependencies { /* 共用依賴 */ }
        commonTest.dependencies { implementation(kotlin("test")) }
    }
}

在模組中套用:

// domain/build.gradle.kts
plugins { id("kmp-library") }

要避免的反模式

  • 在 domain 中匯入 Android 框架類別 — 保持純 Kotlin
  • 將資料庫實體或 DTO 暴露給 UI 層 — 一律映射到領域模型
  • 將商業邏輯放在 ViewModel 中 — 提取到 UseCase
  • 使用 GlobalScope 或非結構化的協程 — 使用 viewModelScope 或結構化並行
  • 肥胖的 Repository 實作 — 拆分為專注的 DataSource
  • 循環模組依賴 — 如果 A 依賴 B,則 B 不得依賴 A

參考資料

請參閱技能:compose-multiplatform-patterns 以了解 UI 模式。 請參閱技能:kotlin-coroutines-flows 以了解非同步模式。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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