genkit-production-expert

# Genkit Production Expert ## Overview Build production-grade Firebase Genkit applications including RAG systems, multi-step flows, and tool-calling agents for Node.js, Python, and Go. This skill covers the full lifecycle from project scaffolding and schema validation through flow implementation, local testing with the Genkit Developer UI, and deployment to Firebase Functions or Cloud Run with AI monitoring and OpenTelemetry tracing. ## Prerequisites - Node.js 18+ (TypeScript), Python 3.10+ (Python), or Go 1.21+ (Go) runtime - Genkit CLI and core packages (`npm install genkit @genkit-ai/googleai` for TypeScript) - Google Cloud project with Vertex AI API enabled for Gemini model access - Firebase CLI for Firebase Functions deployments (`npm install -g firebase-tools`) - Zod (TypeScript), Pydantic (Python), or Go structs for input/output schema validation - Environment variables configured for API keys (never hardcoded; use Secret Manager) ## Instructions 1. Analyze the requirements to determine target language, flow complexity (simple, multi-step, or RAG), model selection (Gemini 2.5 Flash vs Pro), and deployment target 2. Initialize the project structure with appropriate config files (`tsconfig.json`, `genkit.config.ts`, or equivalent) 3. Install Genkit core, provider plugins, and schema validation dependencies 4. Define input/output schemas using Zod, Pydantic, or Go structs to enforce type safety at runtime 5. Implement the Genkit flow using `ai.defineFlow()` with mod...