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
- Initialize Firebase (or validate an existing repo): Hosting/Functions/Firestore as required.
- Implement backend integration:
- add a Cloud Function/HTTP endpoint that calls Gemini/Vertex AI - validate inputs and return structured responses
- Configure data and security:
- Firestore rules + indexes - Storage rules (if applicable) - Auth providers and authorization checks
- Deploy and verify:
- deploy Functions/Hosting - run smoke tests against deployed endpoints
- 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/chatfunction, 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