Genkit

Use this skill when the main question is "should this feature become a reusable server-owned AI workflow, and if so what is the smallest Genkit shape worth owning?"

The job is not to dump a long Genkit tutorial, CLI catalog, or Firebase product tour. The job is to frame the current packet, choose one operating mode, define one backend flow boundary, decide whether Genkit is actually the right layer, and route adjacent work away before the skill turns into app SDK wiring, Firebase ops, or generic framework comparison.

Read references/intake-packets-and-fallbacks.md before handling mixed or ambiguous requests. Read references/modes-and-routing.md before choosing a primary mode. Read references/deployment-and-runtime-boundaries.md when runtime choice is the real open question. Read references/evals-and-observability.md when the workflow already exists and confidence is the bottleneck.

When to use this skill

• A backend or full-stack feature needs a reusable AI flow instead of one-off provider calls scattered through route handlers
• The work needs typed input/output contracts, flow ownership, or one AI capability reused across multiple clients, jobs, or surfaces
• The workflow needs tool calling, retrieval, prompt files, structured outputs, evaluation, or local tracing under a server-owned boundary
• The request is clearly about Genkit or server-side Firebase AI workflow design, not direct app/client SDK integration
• The open question is how to structure, debug, evaluate, or deploy an existing Genkit flow to Firebase, Cloud Run, or another backend runtime