m3-expressive
openbooklet.com/s/m3-expressiveopenbooklet.com/s/m3-expressive@1.0.0GET /api/v1/skills/m3-expressiveMaterial 3 Expressive design patterns for Jetpack Compose - expressive theming, motion physics, shape morphing, typography emphasis, color emphasis, and all 28 expressive components.
Analytics integration patterns - event tracking, screen tracking, user properties, Firebase Analytics, custom analytics providers, and privacy compliance.
Core Android development patterns for Kotlin, including coroutines, lifecycle management, and functional programming idioms.
App lifecycle patterns - process death handling, SavedStateHandle, ViewModel restoration, lifecycle-aware components, and background task management.
CI/CD patterns for mobile - GitHub Actions workflows for Android/iOS, Fastlane integration, code signing, artifact publishing, and automated release.
Apple Combine framework for reactive programming. Publishers, subscribers, operators, and error handling.
Core Data for iOS persistence. Data models, fetch requests, background contexts, and SwiftData migration.
Kotlin Coroutines and Flow patterns for structured concurrency, error handling, and async operations.
Kotlin Multiplatform expect/actual patterns for platform-specific APIs. Learn to declare shared interfaces with platform implementations.
iOS testing with XCTest. Unit tests, UI tests, mocks, and test patterns for Swift/SwiftUI.
Dependency Injection for KMP. Koin multiplatform setup, platform modules, and manual DI patterns.
Navigation libraries for KMP. Voyager, Decompose, and platform-specific navigation (Compose Navigation, SwiftUI).
Ktor client for Kotlin Multiplatform. Shared networking layer with platform-specific engines (OkHttp for Android, Darwin for iOS).
Repository pattern for Kotlin Multiplatform. Shared interfaces with platform-specific implementations, clean data layer architecture.
Koin dependency injection patterns for Android with modules, scopes, and ViewModel injection.
Ktor client patterns for Android networking with content negotiation, error handling, and interceptors.
Context compaction strategies for large Android codebases. Optimize token usage while preserving critical context.
V1 instinct-based pattern capture for mobile development. Captures patterns as code is written with immediate feedback.
V2 instinct-based observational learning. Analyzes sessions to extract reusable mobile development patterns across time.
Memory persistence for mobile development context across sessions. Maintains project structure, dependencies, architecture, and test state.
Automated testing workflow with pass@k metrics for mobile development. Detects flaky tests and ensures code reliability.
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