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android-clean-architecture

Android 및 Kotlin Multiplatform 프로젝트를 위한 클린 아키텍처 패턴 — 모듈 구조, 의존성 규칙, UseCase, Repository 및 데이터 계층 패턴. Android 또는 KMP 프로젝트에서 모듈, 계층 또는 데이터 흐름을 구조화할 때 사용합니다.

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이 스킬은 Android 및 Kotlin Multiplatform(KMP) 프로젝트에서 클린 아키텍처를 구현하기 위한 포괄적인 가이드를 제공합니다. 도메인, 데이터, 프레젠테이션 및 앱 모듈의 모듈 경계와 의존성 규칙을 명확히 정의하여 관심사의 분리를 보장합니다. 특히 'domain' 모듈을 순수 Kotlin으로 유지하고, 데이터 계층에서 Room, SQLDelight, Ktor를 사용한 Repository 구현과 Mapper 패턴을 상세히 다룹니다. UseCase 패턴, 오류 처리(Try/Result), 의존성 주입(Koin/Hilt) 설정 방법도 포함되어 있어, 안정적이고 유지보수 가능한 Android/KMP 앱을 구축하는 데 핵심적인 역할을 합니다.

지원 대상~Claude Code~Codex CLI~Cursor
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문서

Android Clean Architecture

Android 및 KMP 프로젝트를 위한 클린 아키텍처 패턴입니다. 모듈 경계, 의존성 역전, UseCase/Repository 패턴, Room, SQLDelight, Ktor를 활용한 데이터 계층 설계를 다룹니다.

활성화 시기

  • Android 또는 KMP 프로젝트 모듈을 구조화할 때
  • UseCase, Repository 또는 DataSource를 구현할 때
  • 계층(domain, data, presentation) 간 데이터 흐름을 설계할 때
  • Koin 또는 Hilt로 의존성 주입을 설정할 때
  • 계층형 아키텍처에서 Room, SQLDelight 또는 Ktor를 사용할 때

모듈 구조

권장 레이아웃

project/
├── app/                  # Android 진입점, DI 연결, Application 클래스
├── core/                 # 공유 유틸리티, 기본 클래스, 오류 타입
├── domain/               # UseCase, 도메인 모델, 저장소 인터페이스 (순수 Kotlin)
├── data/                 # Repository 구현, DataSource, DB, 네트워크
├── presentation/         # 화면, ViewModel, UI 모델, 네비게이션
├── design-system/        # 재사용 가능한 Compose 컴포넌트, 테마, 타이포그래피
└── feature/              # 기능 모듈 (선택 사항, 대규모 프로젝트용)
    ├── auth/
    ├── settings/
    └── profile/

의존성 규칙

app → presentation, domain, data, core
presentation → domain, design-system, core
data → domain, core
domain → core (또는 의존성 없음)
core → (의존성 없음)

중요: domain은 절대 data, presentation 또는 어떤 프레임워크에도 의존해서는 안 됩니다. 순수 Kotlin만 포함합니다.

도메인 계층

UseCase 패턴

각 UseCase는 하나의 비즈니스 작업을 나타냅니다. 깔끔한 호출을 위해 operator fun invoke를 사용하세요:

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

// 반응형 스트림을 위한 Flow 기반 UseCase
class ObserveUserProgressUseCase(
    private val repository: UserRepository
) {
    operator fun invoke(userId: String): Flow<UserProgress> {
        return repository.observeProgress(userId)
    }
}

도메인 모델

도메인 모델은 일반 Kotlin 데이터 클래스이며 프레임워크 애너테이션이 없습니다:

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 }

저장소 인터페이스

도메인에 정의되고 데이터에서 구현됩니다:

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

데이터 계층

Repository 구현

로컬 및 원격 데이터 소스를 조정합니다:

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 패턴

데이터 모델 근처에 확장 함수로 Mapper를 유지하세요:

// 데이터 계층
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 데이터베이스 (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 네트워크 클라이언트 (KMP)

class ItemRemoteDataSource(private val client: HttpClient) {

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

// 콘텐츠 협상을 사용한 HttpClient 설정
val httpClient = HttpClient {
    install(ContentNegotiation) { json(Json { ignoreUnknownKeys = true }) }
    install(Logging) { level = LogLevel.HEADERS }
    defaultRequest { url("https://api.example.com/") }
}

의존성 주입

Koin (KMP 호환)

// 도메인 모듈
val domainModule = module {
    factory { GetItemsByCategoryUseCase(get()) }
    factory { ObserveUserProgressUseCase(get()) }
}

// 데이터 모듈
val dataModule = module {
    single<ItemRepository> { ItemRepositoryImpl(get(), get()) }
    single { ItemLocalDataSource(get()) }
    single { ItemRemoteDataSource(get()) }
}

// 프레젠테이션 모듈
val presentationModule = module {
    viewModelOf(::ItemListViewModel)
    viewModelOf(::DashboardViewModel)
}

Hilt (Android 전용)

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

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

오류 처리

Result/Try 패턴

오류 전파에 Result<T> 또는 사용자 정의 sealed 타입을 사용하세요:

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
}

// ViewModel에서 — UI 상태로 매핑
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) }
    }
}

컨벤션 플러그인 (Gradle)

KMP 프로젝트의 경우 컨벤션 플러그인을 사용하여 빌드 파일 중복을 줄이세요:

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

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

모듈에 적용:

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

피해야 할 안티 패턴

  • domain에서 Android 프레임워크 클래스를 임포트하는 것 — 순수 Kotlin 유지
  • 데이터베이스 엔티티나 DTO를 UI 계층에 노출 — 항상 도메인 모델로 매핑
  • ViewModel에 비즈니스 로직을 넣는 것 — UseCase로 추출
  • GlobalScope 또는 비구조화된 코루틴 사용 — viewModelScope 또는 구조화된 동시성 사용
  • Repository 구현이 너무 비대해지는 것 — 집중된 DataSource로 분할
  • 순환 모듈 의존성 — A가 B에 의존하면 B는 A에 의존해서는 안 됨

참고

UI 패턴은 skill: compose-multiplatform-patterns를 참조하세요. 비동기 패턴은 skill: kotlin-coroutines-flows를 참조하세요.

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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