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firebase-vertex-ai

firebase-vertex-ai

github.com/jeremylongshoreCloud & DevOps2.9k51 installsUpdated 2d ago
Repository

About firebase-vertex-ai

A skill for designing, implementing, and deploying Firebase apps, including secure backend calls to Gemini or Vertex AI. It covers Auth, Firestore, Functions, Hosting, security configuration, and deployment verification.

Key features

  1. 01Initialize or validate Firebase projects
  2. 02Call Gemini or Vertex AI from a backend
  3. 03Configure Firestore rules and indexes
  4. 04Manage secrets and IAM permissions securely
  5. 05Deploy and run smoke tests
  6. 06Add logging, metrics, and alerts

Use cases

  1. 01Build a Gemini chat endpoint on Firebase
  2. 02Implement a Firestore RAG flow with citations
  3. 03Deploy and verify Firebase Functions and Hosting

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill firebase-vertex-ai

After installing, type/firebase-vertex-aiin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

ARD.md
PRD.md
references/
errors.md
examples.md
implementation.md
scripts/
init-firebase.sh

Details

From the project README

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

Comments

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