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.
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/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
