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Android Clean Architecture

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Fmarzochi
android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects: module structure, dependency rules, UseCases, Repositories, and data layer patterns.

Overview

PublisherFmarzochi
RepositoryEGC
Skill nameandroid-clean-architecture
Stars
68
Forks
49
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Fmarzochi on GitHub. Read the source before you install it.

Installation

Install the Android Clean Architecture AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Fmarzochi/EGC/tree/main/skills/architecture/android-clean-architecture
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Fmarzochi/EGC.git /tmp/EGC
mkdir -p .claude/skills
cp -r /tmp/EGC/skills/architecture/android-clean-architecture .claude/skills/android-clean-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Android Clean Architecture in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Android Clean Architecture on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Android Clean Architecture is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Android Clean Architecture

Clean Architecture patterns for Android and KMP projects. Covers module boundaries, dependency inversion, UseCase/Repository patterns, and data layer design with Room, SQLDelight, and Ktor.

When to Activate

  • Structuring Android or KMP project modules
  • Implementing UseCases, Repositories, or DataSources
  • Designing data flow between layers (domain, data, presentation)
  • Setting up dependency injection with Koin or Hilt
  • Working with Room, SQLDelight, or Ktor in a layered architecture

Module Structure

Recommended Layout

project/
├── app/                  # Android entry point, DI wiring, Application class
├── core/                 # Shared utilities, base classes, error types
├── domain/               # UseCases, domain models, repository interfaces (pure Kotlin)
├── data/                 # Repository implementations, DataSources, DB, network
├── presentation/         # Screens, ViewModels, UI models, navigation
├── design-system/        # Reusable Compose components, theme, typography
└── feature/              # Feature modules (optional, for larger projects)
    ├── auth/
    ├── settings/
    └── profile/

Dependency Rules

app → presentation, domain, data, core
presentation → domain, design-system, core
data → domain, core
domain → core (or no dependencies)
core → (nothing)

Critical: domain must NEVER depend on data, presentation, or any framework. It contains pure Kotlin only.

Domain Layer

UseCase Pattern

Each UseCase represents one business operation. Use operator fun invoke for clean call sites:

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

// Flow-based UseCase for reactive streams
class ObserveUserProgressUseCase(
    private val repository: UserRepository
) {
    operator fun invoke(userId: String): Flow<UserProgress> {
        return repository.observeProgress(userId)
    }
}

Domain Models

Domain models are plain Kotlin data classes: no framework annotations:

kotlin
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 Interfaces

Defined in domain, implemented in data:

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

Data Layer

Repository Implementation

Coordinates between local and remote data sources:

kotlin
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 Pattern

Keep mappers as extension functions near the data models:

kotlin
// In data layer
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 Database (Android)

kotlin
@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)

sql
-- 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 Network Client (KMP)

kotlin
class ItemRemoteDataSource(private val client: HttpClient) {

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

// HttpClient setup with 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-friendly)

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

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

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

Hilt (Android-only)

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

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

Error Handling

Result/Try Pattern

Use Result<T> or a custom sealed type for error propagation:

kotlin
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
}

// In ViewModel: map to UI state
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 Plugins (Gradle)

For KMP projects, use convention plugins to reduce build file duplication:

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

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

Apply in modules:

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

Anti-Patterns to Avoid

  • Importing Android framework classes in domain: keep it pure Kotlin
  • Exposing database entities or DTOs to the UI layer: always map to domain models
  • Putting business logic in ViewModels: extract to UseCases
  • Using GlobalScope or unstructured coroutines: use viewModelScope or structured concurrency
  • Fat repository implementations: split into focused DataSources
  • Circular module dependencies: if A depends on B, B must not depend on A

References

See skill: compose-multiplatform-patterns for UI patterns. See skill: kotlin-coroutines-flows for async patterns.

Frequently asked questions

What does the Android Clean Architecture AI skill do?

Clean Architecture patterns for Android and Kotlin Multiplatform projects: module structure, dependency rules, UseCases, Repositories, and data layer patterns.

Why use Android Clean Architecture on TypingMind?

Because you install it once and use it with any model. Android Clean Architecture is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Android Clean Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Fmarzochi/EGC/tree/main/skills/architecture/android-clean-architecture. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Android Clean Architecture?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Android Clean Architecture?

As many as you like. As long as a model supports skills, you can use Android Clean Architecture with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Android Clean Architecture AI skill free?

Yes. It is published on GitHub by Fmarzochi under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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