AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 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;
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/") }
}
// 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)
}
@Module
@InstallIn(SingletonComponent::class)
abstract class RepositoryModule {
@Binds
abstract fun bindItemRepository(impl: ItemRepositoryImpl): ItemRepository
}
@HiltViewModel
class ItemListViewModel @Inject constructor(
private val getItems: GetItemsByCategoryUseCase
) : ViewModel()
Use Result<T> or a custom sealed type for error propagation:
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) }
}
}
For KMP projects, use convention plugins to reduce build file duplication:
// 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:
// domain/build.gradle.kts
plugins { id("kmp-library") }
domain — keep it pure KotlinGlobalScope or unstructured coroutines — use viewModelScope or structured concurrencySee skill: compose-multiplatform-patterns for UI patterns.
See skill: kotlin-coroutines-flows for async patterns.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and
日本語の概要は準備中です。原文の説明を表示しています。
12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견 반영, 답변서 점검, AI 사용 고지.
日本語の概要は準備中です。原文の説明を表示しています。
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정.
日本語の概要は準備中です。原文の説明を表示しています。
Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.
日本語の概要は準備中です。原文の説明を表示しています。