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android-clean-architecture

Clean Architecture-Muster für Android- und Kotlin-Multiplatform-Projekte – Modulstruktur, Abhängigkeitsregeln, UseCases, Repositories und Datenzugriffsmuster. Zu aktivieren bei der Strukturierung von Modulen, Schichten oder Datenflüssen in Android- oder KMP-Projekten.

Was ist android-clean-architecture?

Diese Skill beschreibt die Implementierung von Clean Architecture für Android und Kotlin Multiplatform (KMP) Projekte. Sie definiert eine klare Modulstruktur mit strikten Abhängigkeitsregeln: Die domain-Schicht ist komplett framework-frei und darf niemals von data, presentation oder Android-Frameworks abhängen. Die Skill behandelt zentrale Patterns wie UseCases, Repository-Interfaces und -Implementierungen, DataSources, Mapper und Dependency Injection mit Koin oder Hilt. Sie liefert konkrete Codebeispiele für Room, SQLDelight und Ktor sowie für Fehlerbehandlung mit sealed Klassen. Entwickler können diese Vorlage nutzen, um wartbare, testbare und skalierbare Apps zu bauen. Unterstützte Plattformen sind Android und KMP (iOS, Android). Die Skill eignet sich für die Projektinitialisierung, Modulstrukturierung und Datenflussarchitektur.

Funktioniert mit~Claude Code~Codex CLI~Cursor
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Dokumentation

Android Clean Architecture

Clean Architecture-Muster für Android- und KMP-Projekte. Behandelt Modulgrenzen, Dependency Inversion, UseCase-/Repository-Patterns und Datenzugriff mit Room, SQLDelight und Ktor.

Wann aktivieren

  • Strukturierung von Android- oder KMP-Projektmodulen
  • Implementierung von UseCases, Repositories oder DataSources
  • Entwurf des Datenflusses zwischen Schichten (domain, data, presentation)
  • Einrichtung von Dependency Injection mit Koin oder Hilt
  • Arbeit mit Room, SQLDelight oder Ktor in einer geschichteten Architektur

Modulstruktur

Empfohlenes Layout

project/
├── app/                  # Android-Einstiegspunkt, DI-Verdrahtung, Application-Klasse
├── core/                 # Gemeinsame Hilfsmittel, Basisklassen, Fehlertypen
├── domain/               # UseCases, Domänenmodelle, Repository-Schnittstellen (reines Kotlin)
├── data/                 # Repository-Implementierungen, DataSources, DB, Netzwerk
├── presentation/         # Screens, ViewModels, UI-Modelle, Navigation
├── design-system/        # Wiederverwendbare Compose-Komponenten, Theme, Typografie
└── feature/              # Feature-Module (optional, für größere Projekte)
    ├── auth/
    ├── settings/
    └── profile/

Abhängigkeitsregeln

app → presentation, domain, data, core
presentation → domain, design-system, core
data → domain, core
domain → core (oder keine Abhängigkeiten)
core → (nichts)

Wichtig: domain darf NIEMALS von data, presentation oder einem Framework abhängen. Es enthält nur reines Kotlin.

Domain-Schicht

UseCase-Muster

Jeder UseCase repräsentiert einen Geschäftsvorgang. Verwenden Sie operator fun invoke für saubere Aufrufe:

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

// Flow-basierter UseCase für reaktive Streams
class ObserveUserProgressUseCase(
    private val repository: UserRepository
) {
    operator fun invoke(userId: String): Flow<UserProgress> {
        return repository.observeProgress(userId)
    }
}

Domänenmodelle

Domänenmodelle sind einfache Kotlin-Datenklassen – keine Framework-Annotationen:

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-Schnittstellen

Definiert in domain, implementiert in data:

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

Data-Schicht

Repository-Implementierung

Koordiniert zwischen lokalen und entfernten Datenquellen:

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-Muster

Halten Sie Mapper als Erweiterungsfunktionen in der Nähe der Datenmodelle:

// In data-Schicht
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-Datenbank (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-Netzwerkclient (KMP)

class ItemRemoteDataSource(private val client: HttpClient) {

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

// HttpClient-Konfiguration mit Content Negotiation
val httpClient = HttpClient {
    install(ContentNegotiation) { json(Json { ignoreUnknownKeys = true }) }
    install(Logging) { level = LogLevel.HEADERS }
    defaultRequest { url("https://api.example.com/") }
}

Dependency Injection

Koin (KMP-kompatibel)

// Domain-Modul
val domainModule = module {
    factory { GetItemsByCategoryUseCase(get()) }
    factory { ObserveUserProgressUseCase(get()) }
}

// Data-Modul
val dataModule = module {
    single<ItemRepository> { ItemRepositoryImpl(get(), get()) }
    single { ItemLocalDataSource(get()) }
    single { ItemRemoteDataSource(get()) }
}

// Presentation-Modul
val presentationModule = module {
    viewModelOf(::ItemListViewModel)
    viewModelOf(::DashboardViewModel)
}

Hilt (nur 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()

Fehlerbehandlung

Result-/Try-Muster

Verwenden Sie Result<T> oder einen benutzerdefinierten sealed-Typ zur Fehlerweitergabe:

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
}

// Im ViewModel – Abbildung auf UI-Zustand
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) }
    }
}

Konventions-Plugins (Gradle)

Verwenden Sie für KMP-Projekte Konventions-Plugins, um Build-Datei-Duplikate zu reduzieren:

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

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

In Modulen anwenden:

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

Anti-Patterns (zu vermeiden)

  • Importieren von Android-Framework-Klassen in domain – halten Sie es rein in Kotlin
  • Datenbank-Entitäten oder DTOs an die UI-Schicht weitergeben – immer auf Domänenmodelle abbilden
  • Geschäftslogik in ViewModels unterbringen – in UseCases auslagern
  • Verwendung von GlobalScope oder unstrukturierten Coroutinen – viewModelScope oder strukturierte Nebenläufigkeit verwenden
  • Fette Repository-Implementierungen – in fokussierte DataSources aufteilen
  • Zirkuläre Modul-Abhängigkeiten – wenn A von B abhängt, darf B nicht von A abhängen

Referenzen

Siehe Skill: compose-multiplatform-patterns für UI-Muster. Siehe Skill: kotlin-coroutines-flows für asynchrone Muster.

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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