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name: firebase-vertex-ai description: 'Execute firebase platform expert with Vertex AI Gemini integration for Authentication, Firestore, Storage, Functions, Hosting, and AI-powered features. Use when asked to "setup firebase", "deploy to firebase", or "integrate vertex ai with firebase". Trigger with relevant phrases based on skill purpose.

' allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*) version: 2.23.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT tags:

• community
• deployment
• authentication compatibility: Designed for Claude Code

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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 add retries/backoff for transient errors.

Examples

Example: Gemini-backed chat API on Firebase

• Request: “Deploy Hosting + a Function that powers a Gemini chat endpoint.”
• Result: /api/chat function, Secret Manager wiring, and smoke tests.

Example: Firestore-powered RAG

• Request: “Build a RAG flow that embeds docs and answers with citations.”
• Result: ingestion plan, embedding + index strategy, and evaluation prompts.

Resources

• Full detailed guide (kept for reference): ${CLAUDE_SKILL_DIR}/references/SKILL.full.md
• Firebase docs: https://firebase.google.com/docs
• Cloud Functions for Firebase: https://firebase.google.com/docs/functions
• Vertex AI docs: https://cloud.google.com/vertex-ai/docs