senior-mobile（高级手机）

Senior Mobile Developer

Expert mobile application development across iOS, Android, React Native, and Flutter.

Keywords

mobile, ios, android, react-native, flutter, swift, kotlin, swiftui, jetpack-compose, expo-router, zustand, app-store, performance, offline-first

Quick Start

# Scaffold a React Native project
python scripts/mobile_scaffold.py --platform react-native --name MyApp

# Build for production
python scripts/build.py --platform ios --env production

# Generate App Store metadata
python scripts/store_metadata.py --screenshots ./screenshots

# Profile rendering performance
python scripts/profile.py --platform android --output report.html

Tools

Platform Decision Matrix

Workflow 1: Scaffold a React Native App (Expo Router)

1. Generate project -- python scripts/mobile_scaffold.py --platform react-native --name MyApp
2. Verify directory structure matches this layout: src/ ├── app/ # Expo Router file-based routes │ ├── (tabs)/ # Tab navigation group │ ├── auth/ # Auth screens │ └── _layout.tsx # Root layout ├── components/ │ ├── ui/ # Reusable primitives (Button, Input, Card) │ └── features/ # Domain components (ProductCard, UserAvatar) ├── hooks/ # Custom hooks (useAuth, useApi) ├── services/ # API clients and storage ├── stores/ # Zustand state stores └── utils/ # Helpers
3. Configure navigation in app/_layout.tsx with Stack and Tabs.
4. Set up state management with Zustand + AsyncStorage persistence.
5. Validate -- Run the app on both iOS simulator and Android emulator. Confirm navigation and state persistence work.

Workflow 2: Build a SwiftUI Feature (iOS)

1. Create the View using NavigationStack, @StateObject for ViewModel binding, and .task for async data loading.
2. Create the ViewModel as @MainActor class with @Published properties. Inject services via protocol for testability.
3. Wire data flow: View observes ViewModel -> ViewModel calls Service -> Service returns data -> ViewModel updates @Published -> View re-renders.
4. Add search/refresh: .searchable(text:) for filtering, .refreshable for pull-to-refresh.
5. Validate -- Run in Xcode previews first, then simulator. Confirm async loading, error states, and empty states all render correctly.

Example: SwiftUI ViewModel Pattern

@MainActor
class ProductListViewModel: ObservableObject {
    @Published private(set) var products: [Product] = []
    @Published private(set) var isLoading = false
    @Published private(set) var error: Error?

    private let service: ProductServiceProtocol

    init(service: ProductServiceProtocol = ProductService()) {
        self.service = service
    }

    func loadProducts() async {
        isLoading = true
        error = nil
        do {
            products = try await service.fetchProducts()
        } catch {
            self.error = error
        }
        isLoading = false
    }
}

Workflow 3: Build a Jetpack Compose Feature (Android)

1. Create the Composable screen with Scaffold, TopAppBar, and state collection via collectAsStateWithLifecycle().
2. Handle UI states with a sealed interface: Loading, Success<T>, Error.
3. Create the ViewModel with @HiltViewModel, MutableStateFlow, and repository injection.
4. Build list UI using LazyColumn with key parameter for stable identity and Arrangement.spacedBy() for spacing.
5. Validate -- Run on emulator. Confirm state transitions (loading -> success, loading -> error -> retry) work correctly.

Example: Compose UiState Pattern

sealed interface UiState<out T> {
    data object Loading : UiState<Nothing>
    data class Success<T>(val data: T) : UiState<T>
    data class Error(val message: String) : UiState<Nothing>
}

@HiltViewModel
class ProductListViewModel @Inject constructor(
    private val repository: ProductRepository
) : ViewModel() {
    private val _uiState = MutableStateFlow<UiState<List<Product>>>(UiState.Loading)
    val uiState: StateFlow<UiState<List<Product>>> = _uiState.asStateFlow()

    fun loadProducts() {
        viewModelScope.launch {
            _uiState.value = UiState.Loading
            repository.getProducts()
                .catch { e -> _uiState.value = UiState.Error(e.message ?: "Unknown error") }
                .collect { products -> _uiState.value = UiState.Success(products) }
        }
    }
}

Workflow 4: Optimize Mobile Performance

1. Profile -- python scripts/profile.py --platform <ios|android> --output report.html
2. Apply React Native optimizations:

- Use FlatList with keyExtractor, initialNumToRender=10, windowSize=5, removeClippedSubviews=true - Memoize components with React.memo and handlers with useCallback - Supply getItemLayout for fixed-height rows to skip measurement

1. Apply native iOS optimizations:

- Implement prefetchItemsAt for image pre-loading in collection views

1. Apply native Android optimizations:

- Set setHasFixedSize(true) and setItemViewCacheSize(20) on RecyclerViews

1. Validate -- Re-run profiler and confirm frame drops reduced and startup time improved.

Workflow 5: Submit to App Store / Play Store

1. Generate metadata -- python scripts/store_metadata.py --screenshots./screenshots
2. Build release -- python scripts/build.py --platform ios --env production
3. Review the generated listing (title, description, keywords, screenshots).
4. Upload via Xcode (iOS) or Play Console (Android).
5. Validate -- Monitor review status and address any rejection feedback.

Reference Materials

Troubleshooting

Success Criteria

• App startup time under 2 seconds on cold launch (measured on mid-range devices, both iOS and Android).
• Crash-free rate above 99.5% across all supported OS versions, tracked via Crashlytics or Sentry.
• Frame rendering at 60 fps (16ms per frame) for scrolling lists and animations, with zero jank frames during typical user flows.
• Bundle size under 50 MB for the initial download (excluding on-demand resources), verified before each release.
• Performance analyzer score of 75+ (Grade B or above) when running app_performance_analyzer.py against the project.
• Zero critical issues and fewer than 5 warnings reported by the performance analyzer before submitting to app stores.
• App Store / Play Store approval on first submission with complete metadata, correct privacy labels, and proper age rating, validated using store_metadata_generator.py.

Scope & Limitations

This skill covers:

• Scaffolding production-ready mobile projects for React Native (Expo Router), Flutter, iOS native (SwiftUI), and Android native (Jetpack Compose).
• Static performance analysis including image asset sizing, re-render detection, memory leak patterns, and bundle size estimation.
• App Store and Play Store metadata generation including titles, keywords, privacy labels, age ratings, and submission checklists.
• Platform-specific architecture patterns (MVVM, state management, navigation).

This skill does NOT cover:

• Backend API development or server-side logic (see senior-backend and senior-fullstack skills).
• CI/CD pipeline configuration for mobile builds and automated distribution (see senior-devops and release-orchestrator skills).
• UI/UX design systems, accessibility auditing, or design token management (see senior-frontend and design-auditor skills).
• Runtime profiling with native tools (Xcode Instruments, Android Studio Profiler) -- the analyzer performs static code analysis only, not live device profiling.

Integration Points

Tool Reference

mobile_scaffold.py

Purpose: Scaffold a production-ready mobile project with proper directory structure, navigation setup, state management, and base configuration files.

Usage:

python scripts/mobile_scaffold.py <name> --platform <platform> [options]

Parameters:

Example:

# Scaffold a React Native app with Zustand state management
python scripts/mobile_scaffold.py MyApp --platform react-native --state zustand

# Scaffold a Flutter app with Riverpod, output as JSON
python scripts/mobile_scaffold.py my-flutter-app --platform flutter --state riverpod --json

# Scaffold an iOS native app in a specific directory
python scripts/mobile_scaffold.py HealthTracker --platform ios-native --output-dir ~/Projects

Output Formats:

• Human-readable (default): Prints the project name, platform, state management choice, created directory path, and a list of all generated files.
• JSON (--json): Returns a JSON object with project_name, platform, typescript, state_management, output_directory, files_created, and generated_at fields.

store_metadata_generator.py

Purpose: Generate structured metadata for App Store (iOS) and Google Play Store (Android) submissions, including title variants, keywords, category recommendations, privacy labels, age rating guidance, and submission checklists.

Usage:

python scripts/store_metadata_generator.py --app-name <name> --category <category> --features <features> [options]

Parameters:

Example:

# Generate metadata for a health app
python scripts/store_metadata_generator.py --app-name "FitTrack" --category health --features "workout,tracking,social" --description "Track your workouts"

# JSON output for CI integration
python scripts/store_metadata_generator.py --app-name "BudgetPal" --category finance --features "payment,offline,biometric,push" --json

Output Formats:

• Human-readable (default): Formatted report with sections for Title Variants, Keywords (with iOS 100-char field), Store Categories, Privacy Labels / Data Safety, Age Rating Guidance, and Submission Checklist.
• JSON (--json): Full metadata object including titles, keywords, categories, descriptions, privacy_labels, age_rating, screenshot_specs, and submission_checklist.

app_performance_analyzer.py

Purpose: Analyze a mobile project directory for common performance issues including oversized image assets, re-render patterns, memory leak patterns, bundle size estimation, and platform-specific anti-patterns.

Usage:

python scripts/app_performance_analyzer.py <project_dir> [options]

Parameters:

Example:

# Analyze with auto-detected platform
python scripts/app_performance_analyzer.py ./my-app

# Analyze a React Native project explicitly
python scripts/app_performance_analyzer.py ./my-app --platform react-native

# JSON output for CI pipeline integration
python scripts/app_performance_analyzer.py ./my-app --platform flutter --json

Output Formats:

• Human-readable (default): Performance score (0-100 with letter grade), issue summary (critical/warning/info counts), bundle size estimate, detailed issues grouped by category, and platform-specific recommendations.
• JSON (--json): Full report object including performance_score, summary, bundle_estimate (source code size, asset size, file counts), issues_by_category, and the flat issues array with category, severity, file, line, and message per issue.