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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionandroid-clean-architectureExecute the skills CLI command in your project's root directory to begin installation:
Fetches android-clean-architecture from affaan-m/everything-claude-code and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate android-clean-architecture. Access via /android-clean-architecture in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
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/
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.
Each UseCase represents one business operation. Use operator fun invoke for clean call sites:
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 are plain Kotlin data classes — no framework annotations:
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 }
Defined in domain, implemented in data:
interface ItemRepository {
suspend fun getItemsByCategory(category: String): Result<List<Item>>
suspend fun saveItem(item: Item): Result<Unit>
fun observeItems(): Flow<List<Item>>
}
Coordinates between local and remote data sources:
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() }
}
}
}
Keep mappers as extension functions near the data models:
// 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
)
@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>>
}
-- 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 Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
asyrafhussin/agent-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
Solid pick for teams standardizing on skills: android-clean-architecture is focused, and the summary matches what you get after install.
android-clean-architecture is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
android-clean-architecture has been reliable in day-to-day use. Documentation quality is above average for community skills.
android-clean-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
android-clean-architecture reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for android-clean-architecture matched our evaluation — installs cleanly and behaves as described in the markdown.
android-clean-architecture has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in android-clean-architecture — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: android-clean-architecture is focused, and the summary matches what you get after install.
android-clean-architecture fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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