Jeremy Firebase

Production-ready Firebase platform operations plugin with Vertex AI Gemini integration.

Overview

Comprehensive automation for Firebase services including Authentication, Cloud Storage, Hosting, Cloud Functions, Analytics, and AI-powered features using Vertex AI Gemini.

Features

Core Firebase Services

• Firebase Authentication - User management, custom claims, OAuth providers
• Cloud Firestore - Document CRUD, queries, security rules, indexes
• Cloud Storage - File uploads, security rules, signed URLs
• Firebase Hosting - Static site deployment, custom domains, SSL
• Cloud Functions - TypeScript/JavaScript functions, HTTP/callable/scheduled triggers
• Firebase Analytics - Event logging, user properties, conversion tracking

AI Integration (Vertex AI Gemini)

• Embeddings Generation - Text-to-vector for semantic search
• Content Analysis - Gemini API for content moderation, classification
• Chat Integration - Conversational AI with Firebase data context
• Model Deployment - Custom AI models with Firebase ML
• RAG Implementation - Retrieval-Augmented Generation with Firestore vector search

DevOps & Operations

• Firebase CLI Automation - Project init, deployment, emulator control
• Environment Management - Multiple Firebase projects (dev, staging, prod)
• Security Rules Testing - Automated testing of Firestore and Storage rules
• Performance Monitoring - Integration with Firebase Performance Monitoring
• Remote Config - Feature flags and A/B testing setup

Installation

/plugin install jeremy-firebase@claude-code-plugins-plus

Prerequisites

• Firebase CLI: npm install -g firebase-tools
• Node.js 18+
• Google Cloud Project with Firebase enabled
• Vertex AI API enabled (for AI features)

Quick Start

Initialize Firebase Project

firebase login
firebase init

Deploy to Firebase

Use the slash commands:

/firebase-deploy-hosting
/firebase-deploy-functions
/firebase-deploy-rules

Environment Configuration

Create .env.local:

FIREBASE_PROJECT_ID=your-project-id
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
VERTEX_AI_LOCATION=us-central1

Available Commands

The plugin provides slash commands for common Firebase operations:

• /firebase-init-project - Initialize new Firebase project
• /firebase-deploy-hosting - Deploy static site to Firebase Hosting
• /firebase-deploy-functions - Deploy Cloud Functions
• /firebase-deploy-rules - Deploy Firestore and Storage security rules
• /firebase-auth-setup - Configure Firebase Authentication providers
• /firebase-vertex-ai-embeddings - Generate embeddings with Vertex AI
• /firebase-firestore-query - Execute Firestore queries
• /firebase-storage-upload - Upload files to Cloud Storage

See /firebase-help for complete command list.

Agent Skills

This plugin includes Agent Skills that automatically activate based on trigger phrases:

firebase-vertex-ai

Triggers: "integrate vertex ai", "add gemini to firebase", "generate embeddings"

Automatically sets up Vertex AI Gemini integration with Firebase, including:

• Content moderation for user-generated content
• Semantic search with embeddings
• Conversational AI chatbots
• Image analysis with Gemini Vision

Architecture

Firebase Project
├── Authentication (user management)
├── Firestore (NoSQL database)
├── Cloud Storage (file storage)
├── Cloud Functions (serverless backend)
├── Firebase Hosting (static site hosting)
└── Vertex AI Integration
    ├── Gemini 2.0 Flash (content analysis)
    ├── Text Embeddings (semantic search)
    └── Custom Models (ML deployment)

Example Use Cases

1. User Registration with AI Content Moderation

// Cloud Function triggered on user creation
exports.moderateUserProfile = functions.firestore
  .document('users/{userId}')
  .onCreate(async (snap, context) => {
    const {VertexAI} = require('@google-cloud/vertexai');
    const vertex = new VertexAI({project: 'PROJECT_ID', location: 'us-central1'});
    const model = vertex.getGenerativeModel({model: 'gemini-2.5-flash'});

    const userBio = snap.data().bio;
    const result = await model.generateContent(`Moderate this user bio for inappropriate content: ${userBio}`);

    if (result.response.text().includes('INAPPROPRIATE')) {
      await snap.ref.update({status: 'pending_review'});
    }
  });

2. Semantic Search with Firestore

// Generate embeddings and store in Firestore
const embedding = await generateEmbedding(documentText);
await firestore.collection('documents').add({
  text: documentText,
  embedding: embedding,
  createdAt: admin.firestore.FieldValue.serverTimestamp()
});

// Query by similarity
const queryEmbedding = await generateEmbedding(searchQuery);
const results = await vectorSearch(queryEmbedding, 10);

3. AI-Powered Chatbot

// Cloud Function for chat endpoint
exports.chat = functions.https.onCall(async (data, context) => {
  const {VertexAI} = require('@google-cloud/vertexai');
  const vertex = new VertexAI({project: 'PROJECT_ID'});
  const model = vertex.getGenerativeModel({model: 'gemini-2.5-flash'});

  // Get conversation history from Firestore
  const history = await getConversationHistory(context.auth.uid);

  const result = await model.generateContent({
    contents: [...history, {role: 'user', parts: [{text: data.message}]}]
  });

  return {reply: result.response.text()};
});