Module 08 · Scrape, local and chaining

Use scrape_emails_from_website with url "acmedental.com".
Return every address found, and tell me which are role addresses
(info@, sales@, hello@) and which look like named people.
Then verify_email on the named ones only.

Use scrape_phones_from_website with url "acmedental.com".
Return every number found with the page it appeared on.
Then validate_whatsapp on any mobile-format number.

Use google_maps_listings with:
- query "dental clinics"
- location "Austin, TX"
- include_reviews true
- max_results 50

Return business name, website, phone, rating and review count.
Sort by review count.

For https://linkedin.com/in/janedoe:
1. linkedin_profile_enrich
2. find_work_email with organization_name from step 1
3. verify_email on the result — stop if undeliverable

Return: name, title, company, email, status. Report credits used.

For stripe.com:
1. enrich_linkedin_page with identifier "stripe.com"
2. find_company_techstack with domain "stripe.com"
3. find_company_website_traffic with domain "stripe.com"
4. company_product_launch with domain "stripe.com", details "last 12 months"

Return a 150-word brief plus one reason they would care about us.

1. linkedin_post_commenters, post_url <url>, sort_order "Most recent"
2. Filter to VP/Head/Director titles at 51-500 employee companies.
   Drop the rest before spending anything.
3. First 25 survivors: find_work_email, then verify_email.
   Skip anyone with no result — do not guess.

Return a table with name, title, company, email, status.

1. google_maps_listings, query "dental clinics", location "Austin, TX",
   max_results 50
2. For the 20 with the most reviews: scrape_emails_from_website on each website
3. verify_email on every named address found

Return: business, website, email, status, review count.

1. google_maps_listings — query "dental clinics", location "Austin, TX",
   max_results 50. 0.3 credits for the search.
2. Drop any listing with no website. Rank the rest by review count.
3. For the top 20: scrape_emails_from_website and
   scrape_phones_from_website — 1 credit per business.
4. Split addresses into named (first.last@) and role (info@, hello@).
   verify_email on the named ones only.
5. Write to a new Google Sheet "Austin dental — <today>" with columns:
   business, website, named email, status, role email, phone, rating,
   reviews. Leave cells blank where nothing was published — do not guess.

1. Pull Attio companies with no associated contact and no phone on record.
2. For each domain: scrape_emails_from_website, then
   scrape_phones_from_website. 1 credit per company — confirm the total.
3. verify_email on any named address found. Skip role addresses.
4. Show me the diff table first: company, found email, status, found phone.
5. On my go-ahead, write the verified email and the phone to the Attio
   company record. Do not overwrite anything already there.

1. Here are 60 exhibitor domains from <conference>.
2. scrape_emails_from_website on each — 30 credits total, confirm first.
3. For any company where nothing was published, fall back to
   find_people_within_company with job_title ["Head of Marketing",
   "VP Marketing"], max_profiles 2, then find_work_email on those.
4. verify_email on everything found.
5. Return one CSV: company, domain, contact name if any, email, source
   (scraped or enriched), status. Tell me the coverage rate for each source.