mouchegmouradian/claude-code-skills Skills

Create production-quality Android applications following Google's official architecture guidance and NowInAndroid best practices. Use when building Android apps with Kotlin, Jetpack Compose, MVVM architecture, Hilt dependency injection, Room database, or multi-module projects. Triggers on requests to create Android projects, screens, ViewModels, repositories, feature modules, or when asked about Android architecture patterns.

Create production-quality iOS applications following Apple's official guidance and modern Swift patterns. Use when building iOS apps with Swift, SwiftUI, MVVM, Swift Concurrency (actors, async/await), SwiftData, or multi-module projects. Triggers on requests to create iOS projects, screens, ViewModels, repositories, feature modules, actors, or iOS architecture patterns.

Build comprehensive product requirements documents (PRDs) and technical RFC specifications for software projects. Guides the user through structured discovery — from problem statement and user personas to technical architecture, data models, and implementation RFCs. Use this skill whenever the user wants to plan a new product, define requirements for a feature, create technical specs, write an RFC, break down a project into implementable pieces, or prepare documentation for AI-assisted coding (Claude Code, Cursor, etc.). Also use when the user says things like "help me spec this out", "let's plan this project", "write a PRD", "create an RFC", "break this into tasks", or "I need to define requirements for...".

Help users integrate the ZETIC Melange (MLange) on-device AI SDK into Android (Kotlin/Java) and iOS (Swift) mobile apps. Use this skill whenever the user mentions Zetic, MLange, Melange, ZeticMLangeModel, ZeticMLangeLLMModel, ZeticMLangeHFModel, on-device NPU inference for mobile, or deploying ML models to Android/iOS with NPU acceleration. Also trigger when users ask about running LLMs on-device with GGUF quantization, mobile NPU model deployment, or converting PyTorch/ONNX models for mobile inference. Covers general model inference, LLM inference (streaming token generation), and HuggingFace model loading.