Build production Firebase Genkit applications including RAG systems, multi-step flows, and tool calling for Node.js/Python/Go. Deploy to Firebase Functions or Cloud Run with AI monitoring. Use when asked to "create genkit flow" or "implement RAG". Trigger with relevant phrases based on skill purpose.
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
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
Initialize the project structure with appropriate config files (tsconfig.json, genkit.config.ts, or equivalent)
Install Genkit core, provider plugins, and schema validation dependencies
Define input/output schemas using Zod, Pydantic, or Go structs to enforce type safety at runtime
Implement the Genkit flow using ai.defineFlow() with model configuration, temperature tuning, and token limits
Add tool definitions using ai.defineTool() with scoped schemas for each external capability the flow requires
For RAG flows: implement a retriever using ai.defineRetriever() with embedding generation (text-embedding-gecko) and vector database integration
Configure error handling for safety blocks (SAFETY_BLOCK), quota exceeded (QUOTA_EXCEEDED), and provider timeouts
Enable OpenTelemetry tracing with custom span attributes for cost and latency tracking
Test locally using the Genkit Developer UI, then deploy to Firebase Functions or Cloud Run with auto-scaling configuration
See ${CLAUDE_SKILL_DIR}/references/how-it-works.md for the phased workflow and ${CLAUDE_SKILL_DIR}/references/production-best-practices-applied.md for the production checklist.
Output
Complete Genkit flow implementation with typed schemas and model bindings
Tool definitions with Zod/Pydantic-validated inputs and outputs
Retriever configuration for RAG flows (embeddings, vector search, context injection)
Deployment configuration: Firebase Functions (firebase.json) or Cloud Run service YAML
Add descriptive error messages to schema; validate inputs before calling ai.generate()
Retriever returns empty results
Vector database query found no matches above similarity threshold
Lower similarity threshold; verify embeddings are indexed; check embedding model version match
Deployment timeout
Cold start exceeds Firebase Functions 60s limit
Increase memory allocation; use Cloud Run for long-running flows; enable min instances > 0
See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.
Examples
Scenario 1: Question-Answering Flow -- Create a Genkit flow using Gemini 2.5 Flash with Zod input/output schemas. Set temperature to 0.3 for factual responses. Deploy to Firebase Functions with token usage monitoring. Expected latency: under 2 seconds per query.
Scenario 2: RAG Document Search -- Implement a retriever with text-embedding-gecko embeddings connected to Firestore vector search. Build a RAG flow that retrieves top-5 relevant documents, injects them as context, and generates grounded answers with source citations. Include context caching for repeated queries.
Scenario 3: Multi-Tool Agent -- Define weather and calendar tools with typed schemas. Create an agent flow that routes user queries to appropriate tools, handles multi-turn conversations, and traces each tool execution for debugging. Deploy to Cloud Run with auto-scaling (2-10 instances).
See ${CLAUDE_SKILL_DIR}/references/workflow-examples.md for complete code examples.
