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Claude-skill-registry firebase-vertex-ai

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/firebase-vertex-ai" ~/.claude/skills/majiayu000-claude-skill-registry-firebase-vertex-ai && rm -rf "$T"
manifest: skills/data/firebase-vertex-ai/SKILL.md
source content

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