What problem does it solve? Adding generative AI to client apps usually requires managing a backend proxy for API keys and model calls. This Skill guides you through integrating Firebase AI Logic so your app can call Gemini models directly from client-side SDKs with proper provisioning, security, and platform-specific setup. ## Core Features & Use Cases - Setup & Provisioning: Walks through Firebase CLI installation, project selection, and the mandatory firebase-tools init ailogic step that enables the Gemini Developer API. - Core Generation Capabilities: Covers text generation, multimodal input (images, audio, video, PDF), multi-turn chat sessions, streaming responses, image generation, and structured JSON output. - Security & Production Readiness: Enforces App Check setup to protect API quota and recommends Remote Config for updating model names without redeploying. - Use Case: You are building a Flutter app that summarizes user-uploaded photos. Use this Skill to provision AI Logic, initialize the firebase_ai package with gemini-flash-latest, and implement multimodal inference with App Check enabled. ## Quick Start Ask the assistant to set up Firebase AI Logic in your web app and generate text with the Gemini Developer API.
