firebase-vertex-ai

# Firebase Vertex AI

Operate Firebase projects end-to-end (Auth, Firestore, Functions, Hosting) and integrate Gemini/Vertex AI safely for AI-powered features.

## Overview

Use this skill to design, implement, and deploy Firebase applications that call Vertex AI/Gemini from Cloud Functions (or other GCP services) with secure secrets handling, least-privilege IAM, and production-ready observability.

## Prerequisites

- Node.js runtime and Firebase CLI access for the target project
- A Firebase project (billing enabled for Functions/Vertex AI as needed)
- Vertex AI API enabled and permissions to call Gemini/Vertex AI from your backend
- Secrets managed via env vars or Secret Manager (never in client code)

## Instructions

1. Initialize Firebase (or validate an existing repo): Hosting/Functions/Firestore as required.
2. Implement backend integration:
   - add a Cloud Function/HTTP endpoint that calls Gemini/Vertex AI
   - validate inputs and return structured responses
3. Configure data and security:
   - Firestore rules + indexes
   - Storage rules (if applicable)
   - Auth providers and authorization checks
4. Deploy and verify:
   - deploy Functions/Hosting
   - run smoke tests against deployed endpoints
5. Add ops guardrails:
   - logging/metrics
   - alerting for error spikes
   - basic cost controls (budgets/quotas) where appropriate

## Output

- A deployable Firebase project structure (configs + Functions/Hosting as needed)
- Secure backend code that calls Gemini/Vertex AI (with secrets handled correctly)
- Firestore/Storage rules and index guidance
- A verification checklist (local + deployed) and CI-ready commands

## Error Handling

- Auth failures: identify the principal and missing permission/role; fix with least privilege.
- Billing/API issues: detect which API or quota is blocking and provide remediation steps.
- Firestore rule/index problems: provide minimal repro queries and rule fixes.
- Vertex AI call failures: surface model/region mismatches and