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()};
});

Security Best Practices

Firestore Security Rules

rules_version = '2';
service cloud.firestore {
  match /databases/{database}/documents {
    function isAuthenticated() {
      return request.auth != null;
    }

    function isOwner(userId) {
      return isAuthenticated() && request.auth.uid == userId;
    }

    match /users/{userId} {
      allow read: if isOwner(userId);
      allow write: if isOwner(userId);
    }
  }
}

Storage Security Rules

rules_version = '2';
service firebase.storage {
  match /b/{bucket}/o {
    match /users/{userId}/{allPaths=**} {
      allow read: if request.auth != null;
      allow write: if request.auth.uid == userId
                   && request.resource.size < 10 * 1024 * 1024; // 10MB limit
    }
  }
}

Performance Optimization

• Batch Firestore Operations - Use batch writes for multiple document updates
• Firebase Functions Cold Starts - Implement function warming strategies
• Vertex AI Rate Limits - Implement exponential backoff and retry logic
• Storage Upload Optimization - Use resumable uploads for large files
• Hosting Cache Headers - Configure proper caching in firebase.json

Troubleshooting

Common Issues

Issue: Firebase CLI not found

npm install -g firebase-tools

Issue: Authentication failed

firebase login --reauth

Issue: Vertex AI quota exceeded

• Check quotas in GCP Console
• Implement rate limiting in Cloud Functions
• Use caching to reduce API calls

Issue: Firestore permissions denied

• Check security rules in Firebase Console
• Verify user is authenticated
• Review IAM permissions

Configuration

firebase.json

{
  "firestore": {
    "rules": "firestore.rules",
    "indexes": "firestore.indexes.json"
  },
  "functions": {
    "source": "functions"
  },
  "hosting": {
    "public": "public",
    "ignore": ["firebase.json", "**/.*", "**/node_modules/**"]
  },
  "storage": {
    "rules": "storage.rules"
  }
}

Documentation

For detailed plugin documentation and Firebase integration guides, see:

• Plugin documentation: 000-usermanuals/
• Firebase docs: https://firebase.google.com/docs
• Vertex AI docs: https://cloud.google.com/vertex-ai/docs

License

MIT License - See LICENSE file

Author

Jeremy Longshore

• GitHub: https://github.com/jeremylongshore
• Plugin Marketplace: https://claudecodeplugins.io

Support

• Issues: https://github.com/jeremylongshore/claude-code-plugins/issues
• Discord: https://discord.com/invite/6PPFFzqPDZ (#claude-code channel)

Version: 1.0.0 Category: Integration Plugin Type: AI Instruction + Agent Skills Last Updated: 2025-11-13