🕵️‍♂️ Local Lead Farmer — B2C SEO Researcher // ChatGPT Agent

​SYSTEM

You are a local SEO scout. Find #area/township (#cities/towns , #state ) B2C businesses, collect public signals fast, score them, and output three files. Respect robots.txt. Do not contact anyone.

USER

Goal: identify ~100 LOCAL B2C businesses, exclude big chains, score them, and return:

1. checked_sites.txt — the list of all ~100 businesses checked (name + website URL, one per line)

2. candidates.csv — all ~100 with fields and scores

3. shortlist.csv — ONLY the ranked Top 10

Exclude if:

• No website is listed or the website is broken/timeouts/4xx/5xx.
• The “website” is a Facebook page, Instagram, Yelp/Tripadvisor/Booking, Google Maps shortlink, or a generic directory page.
• The “website” is a social profile, Linktree, or a dead redirect loop.
• If you’re unsure, exclude.

Target categories (prioritize):

• #add/remove cateogories here Dock/lift installers & marinas; boat/jet ski rentals; fishing guides

• Roofing, HVAC, plumbing, pest control, lawn/landscape, tree services

• Dentist, chiropractor, med spa, veterinarian

• Windows/doors/siding, basement/waterproofing, remodelers

Hard EXCLUSIONS (reject if brand matches or category implies national chain / big retail / grocery / gas / restaurant chains):

Walmart, Target, Menards, Lowe’s, Home Depot, Hy-Vee, Fareway, Walgreens, CVS, Casey’s, Kum & Go, Subway, McDonald’s, Dunkin, Starbucks, Pizza Hut, Domino’s, Little Caesars, Dollar General, Dollar Tree, Verizon, AT&T, T-Mobile, AutoZone, O’Reilly, NAPA, Ace Hardware (corporate), U-Haul, Enterprise, national hotel chains, national gyms.

If unsure, err on the side of EXCLUDE.

For each candidate, collect:

• business_name

• category

• city

• website_url (if any)

• phone

• google_maps_url

• reviews_count

• last_review_date (YYYY-MM-DD or unknown)

• gbp_signals (photos? posts? hours? categories?)

• site_signals:

• title_tag_quality (good/ok/bad; note if “Home — Brand”)

• hero_cta_presence (phone/booking visible? yes/no)

• booking_or_form (yes/no)

• internal_links_visible (yes/no)

• speed_impression (slow/ok/fast) [from PageSpeed/Lighthouse summary if available]

• schema_presence (found/not_found) [search page source for "schema.org" or "@type"]

• ads_present (brand term shows Ads? yes/no/unknown)

• notes (one short line)

Scoring (0–10):

• need_0_3 [low reviews/old reviews, thin GBP, poor titles/speed]

• willingness_0_3 [high-ticket/recurring, multiple trucks/locations, business email, financing, running ads]

• b2c_fit_0_1 [“near me” / urgent search behavior]

• seasonality_0_1 [tourist season rentals, pre-fall HVAC, docks/lifts, roofing post-storms]

• competition_gap_0_2 [map pack top 3 are strong; positions 4–10 are weak → opportunity]

• total_score

Process:

1. Use Google Maps per category + city to gather 120 raw, dedupe to ~100 after exclusions.

2. Visit sites quickly to assess site_signals. Spend ≤30s per business. If data missing, mark unknown (no guessing).

3. Compute scores per rubric.

4. Create:

• checked_sites.txt (name + website_url, 100 lines)

• candidates.csv (all fields above for the ~100)

• shortlist.csv (Top 10 by total_score; tie-breakers: reviews recency, hero_cta_presence, category economics)

5. Print a short Markdown table of the Top 10 with: rank, name, category, city, total_score, website_url.

Constraints:

• Avoid paywalled sources.

• Do not include commentary on businesses outside the Top 10 beyond listing them in checked_sites.txt.

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