What problem does it solve? Integrating on-device generative AI into Android apps involves tricky setup details—SDK levels, dependency versions, model availability checks, lifecycle cleanup, and structured output refactoring—that are easy to get wrong without guided steps. ## Core Features & Use Cases - Prompt API Integration: Add the ML Kit GenAI Prompt API dependency and send natural language requests to Gemini Nano on-device. - Structured Output: Refactor string-returning prompts into typed @Generable data classes using the Structured Output API. - Prefix Caching & Optimization: Apply prefix caching for prompts over 200 words and follow prompt optimization best practices. - Use Case: You want to parse emails on-device into sender, title, and summary fields. This Skill guides you to check model availability, refactor your function to return a typed ParsedEmail object, and release the model with close() when the ViewModel is destroyed. ## Quick Start Use the ml-kit-genai-prompt-api skill to add on-device Gemini Nano prompting with structured output to my Android app.