google-maps-scraper-kit: Command for Claude Code

The user wants a Google Maps lead list. Query: $ARGUMENTS

Use the google-maps-scraper skill and follow these steps:

1. Check the engine: curl -s http://localhost:8080/api/v1/jobs. If it is down, run docker compose up -d from the kit root, wait ~10s, and retry.
2. Parse the business type and city from the query. Look up the city's latitude/longitude (as strings).
3. Ask about social profiles before scraping, every time. Ask the user ONE short question: "Want me to also grab their social profiles (Instagram / Facebook / LinkedIn)? Emails are already included by default. (yes / no)" Wait for the answer. Skip the question only if the user already said yes or no to socials in the request. If yes, add --socials in step 4. If no, leave it off. Socials cost no LLM tokens and one extra HTTP fetch per site (see the skill).
4. Create the job with email extraction on ("email": true in the body; emails are the most valuable lead field). Required fields: keywords (location baked into each term), lat/lon (strings), max_time (seconds, default 300), depth (the number in the query, else 5). Easiest path: python3 scripts/scrape.py "<keyword>" --city "<City, ST>" --depth <n> (emails are already on by default; append --socials if the user said yes in step 3).
5. Poll to completion in the background. The Bash tool blocks foreground sleep, so use run_in_background: true and read the output file when notified. Then download the CSV.
6. Save a CSV file. scripts/scrape.py writes results-<id>.csv by default. If you use the raw API, save the parsed lead fields to a .csv yourself. Then present only the 8 lead fields in a clean markdown table: name (title), phone, emails, website, category, address, review_rating, review_count, plus instagram, facebook, linkedin if socials were requested. Drop everything else, especially latitude/longitude, IDs, hours, images and review blobs. Never dump the full 34-column CSV into chat. Show a few sample rows and give the CSV path. Include dropped fields only if the user explicitly asks.

All, every or entire city: add --grid 3 (3 x 3 map tiles, one job at a time, deduped) to get past Google's ~120 results per search. Warn once that it is slower and more likely to trip rate limits, then proceed.

Emails: on by default. The engine visits each business website to find them, which is a little slower. Some businesses publish no email, so coverage is partial. For a deliberately fast run without emails, pass --no-email.

Socials: when the user says yes, scripts/scrape.py … --socials adds Instagram, Facebook and LinkedIn. Extraction costs 0 LLM tokens because it runs in code. It adds ~40–50 tokens per business only if you load the rows into chat. Never WebFetch each website yourself to find socials; that costs thousands of tokens.

Warn, don't block: if the request is high-volume (high depth, many keywords or repeated runs), print ONE short warning that heavy use can get the user's IP temporarily rate-limited by Google, and suggest proxies. Then proceed with the scrape. Do not gate or demand confirmation for a big job. Refuse outright only for clearly abusive use (surveilling individuals, spam, harassment). If the user will store or contact the data, remind them once that it is personal data and that scraping is against Google's Terms of Service.