/google-maps-list-builder

name: google-maps-list-builder
description: Scrape Google Maps for local businesses by category and location, output CSV ready for cold email enrichment. Best for SMB campaigns targeting restaurants, clinics, gyms, salons, contractors, etc. Uses RapidAPI Maps Data API. Output feeds directly into /blitz-list-builder (to find owner contacts) or //list-builder Phase 5 (if you have names already).

Google Maps List Builder

A self-contained tool for scraping business listings from Google Maps. Give it a search query (e.g., "pizza restaurant") and a location (zip code, city, or coordinates), and it returns structured data for every matching business — written to CSV.

How this fits in the cold email flow

Google Maps gives you COMPANIES (name, domain, phone, address, ratings). It does NOT give you PEOPLE. To run cold email:

1. Run this skill → CSV of businesses with `company_domain` 2. **Run `/icp-prompt-builder` on a sample of 50** — tune a qualification prompt to filter out bad fits before paying for downstream enrichment 3. Run `/blitz-list-builder` with the filtered CSV → adds owners/managers to each business 4. Run `/list-builder` (Phase 5, emails) → fills in missing emails 5. Run `/cold-email-starter-kit`'s `smartlead-add-leads.ts` → upload to Smartlead

This skill is only the first step.

Required step: Qualify with /icp-prompt-builder

**This is a required step. Do not skip it.**

Google Maps will happily return 10,000 "pizza restaurants in Illinois," but most of those won't match your actual ICP (maybe you only want 50-200 seat operators, or only ones without online ordering). Before spending on enrichment, sample ~50 results and run `/icp-prompt-builder`:

1. Evaluate 10 results with an AI qualification prompt 2. You flag "this one should be NO, they're a chain franchise" 3. Refine, run next 10 4. Stop when 2 rounds show no corrections 5. Apply tuned prompt to filter the rest of the scrape

**Why required:** downstream owner-finding (via `/blitz-list-builder`) and email waterfall cost $0.10-$0.30 per contact. On a 10,000-business scrape, that's $1K-$3K. Qualifying upfront saves 50-80% of that spend on average.

What You Need Before Starting

1. **Node.js 18+** and **npm** installed 2. **A RapidAPI account** (free tier available) with a subscription to the **Maps Data API**:

• Sign up at https://rapidapi.com
• Subscribe to the API: https://rapidapi.com/alexanderxbx/api/maps-data
• Copy your RapidAPI key from the dashboard (it's in the `X-RapidAPI-Key` header on any endpoint page)

That's it. No Google Cloud account, no OAuth, no billing setup beyond RapidAPI.

Project Setup

Create a new project directory and initialize it:

mkdir google-maps-scraper && cd google-maps-scraper
npm init -y
npm install typescript bottleneck
npm install -D @types/node tsx

Add to `package.json` scripts:

{
  "scripts": {
    "scrape": "tsx src/index.ts"
  }
}

Create `tsconfig.json`:

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "ESNext",
    "moduleResolution": "bundler",
    "esModuleInterop": true,
    "strict": true,
    "outDir": "dist",
    "rootDir": "src",
    "skipLibCheck": true
  },
  "include": ["src"]
}

Set your API key as an environment variable:

export RAPIDAPI_KEY=your_key_here

Or create a `.env` file (add `.env` to `.gitignore`):

RAPIDAPI_KEY=your_key_here

File Structure

google-maps-scraper/
  data/
    us-zip-codes.csv   # 42,734 US zip codes with city, state, lat/lng, population
  src/
    index.ts           # CLI entry point
    client.ts          # RapidAPI Maps Data client with rate limiting
    types.ts           # TypeScript interfaces
    csv.ts             # CSV export
    zips.ts            # Zip code loader (filter by state, city, population)

Bundled Zip Code Database

The repo includes `data/us-zip-codes.csv` — a complete US zip code reference with 42,734 entries. Columns:

zip,primary_city,state,timezone,area_codes,world_region,country,latitude,longitude,irs_estimated_population

This lets you scrape an entire state or metro area without manually listing zip codes. The `src/zips.ts` loader provides filtering by state, city, and minimum population.

Core Files

src/types.ts

export interface SearchParams {
  query: string;       // "pizza restaurant", "dentist", "gym"
  lat?: number;        // Center latitude (optional if using "query in zipcode" format)
  lng?: number;        // Center longitude
  limit?: number;      // Max results per search (default 20, max 20)
  zoom?: number;       // Map zoom level (default 13 = neighborhood)
  country?: string;    // Country code (default "us")
}

export interface Place {
  place_id: string;
  name: string;
  address: string;
  lat: number;
  lng: number;
  rating?: number;
  reviews_count?: number;
  phone?: string;
  website?: string;
  types?: string[];
  category?: string;
}

export interface ScrapeResult {
  query: string;
  location: string;
  total_results: number;
  unique_results: number;
  places: Place[];
  duration_ms: number;
}

src/zips.ts

Loads and filters the bundled zip code CSV. Lets you target by state, city name, or minimum population.

import { readFileSync } from 'fs';
import { join, dirname } from 'path';
import { fileURLToPath } from 'url';

export interface ZipEntry {
  zip: string;
  city: string;
  state: string;
  lat: number;
  lng: number;
  population: number;
}

let cache: ZipEntry[] | null = null;

function loadAll(): ZipEntry[] {
  if (cache) return cache;
  const __dirname = dirname(fileURLToPath(import.meta.url));
  const csvPath = join(__dirname, '..', 'data', 'us-zip-codes.csv');
  const raw = readFileSync(csvPath, 'utf-8');
  const lines = raw.trim().split('\n').slice(1); // skip header

  cache = lines.map(line => {
    // Handle quoted fields (area_codes can contain commas)
    const parts: string[] = [];
    let current =