Agent skill · software engineering · jeremylongshore
genkit-production-expert
Build production Firebase Genkit applications including RAG systems,
Why this skill is useful
Adds executable scripts and specific deployment patterns for Firebase Genkit applications that the AI wouldn't reliably generate on its own.
What it needs
Requires firebase-tools, genkit, go, node, python installed locally. Requires google-cloud account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
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 model configuration, temperature tuning, and token limits 6. Add tool definitions using ai.defineTool() with scoped schemas for each external capability the flow requires 7. For RAG flows: implement a retriever using ai.defineRetriever() with embedding generation (text-embedding-gecko) and vector database integration 8. Configure error handling for safety blocks (SAFETYBLOCK), quota exceeded (QUOTAEXCEEDED), and provider timeouts 9. Enable OpenTelemetry tracing with custom span attributes for cost and latency tracking 10. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/genkit-production-expert--f9066e