| name | local-business-prospecting |
| version | 1.0.0 |
| description | Builds prospect lists of local/hyperlocal businesses (restaurants, dentists, gyms, contractors,
agencies, retail, clinics — anything with a Google Maps presence) by keyword + location, with
enrichment of website, phone, email, tech stack, and reputation. Different ICP shape from
LinkedIn-centric prospecting: queries are "what + where" (e.g. "dentists in Austin"), evidence
is reviews/ratings/category, outreach is call/SMS/local-targeted email. Proactively invoke on
phrases like "find all dentists in", "list of restaurants near", "local business list",
"scrape Google Maps for", "businesses in <city> with <criterion>".
|
| benefits-from | ["richapi-gtm","list-hygiene","cost-optimizer","crm-export"] |
| allowed-tools | ["Bash","Read","Write","Edit","AskUserQuestion"] |
| triggers | ["find all <category> in <location>","list of <category> near <location>","local business list","scrape Google Maps for <category>","hyperlocal prospects","<category> in <city>","businesses in <area> with <criterion>","local lead list"] |
local-business-prospecting
Build a deduplicated, contact-enriched list of local businesses by category + location. This skill is for hyperlocal / SMB / brick-and-mortar sourcing — when the LinkedIn-centric build-prospect-list doesn't fit because there are no titles, no industries-as-IDs, just a category and a city.
Preamble — run first
~/.claude/skills/richapi-gtm-skills/bin/richapi-skills-preflight
If CATALOG_OK: no → stop and run richapi-skills-sync. Do not guess tool names.
When to use this skill vs build-prospect-list
| Signal | Use |
|---|
| Query has a job title or seniority | build-prospect-list |
| Query has a LinkedIn URL or Sales Nav URL | build-prospect-list |
| Query is " in " with no people component | this skill |
| User wants ratings / reviews / hours / address as evidence | this skill |
| User wants website, phone, owner email | this skill (then enrich) |
If both apply (e.g. "dentists in Austin AND their office managers on LinkedIn"), run this skill first to source accounts, then hand off to build-prospect-list with linkedin_company_employees_search for the people layer.
Phase 0 — parse the request
Extract these fields. Ask only for the missing critical ones.
| Slot | Example | Required? | Maps to |
|---|
category | "dentist", "coffee shop", "law firm" | ✅ yes | keyword |
location | "Austin, TX", "Brooklyn, NY", "94110" | ✅ yes | location |
quality_filter | min rating, min review count, "no website" | optional | post-filter |
count_target | 50, 200 | optional, default 50 | max_results |
language | "en", "es" | optional, default en | language |
existing_maps_url | a hand-crafted Maps URL | optional | use google_maps_places_scraper_url instead |
enrich_with | ["website", "email", "tech", "reviews"] | optional, see Phase 4 | follow-up tools |
If the user pasted a Google Maps URL with applied filters (zoomed/sorted/category-filtered), skip Phase 1's keyword path and use google_maps_places_scraper_url instead — it preserves their UI filters.
Phase 1 — source the place list
Read _lib/mcp-catalog.json → decision_tree.local_business for the canonical decision tree. Default path:
- By keyword + location →
google_maps_places_scraper (0.2/place)
- By Maps URL →
google_maps_places_scraper_url (0.2/place)
State the cost out loud before calling:
"Sourcing up to {{count_target}} {{category}} in {{location}}. Cost: ~{{count_target * 0.2}} credits. OK?"
If count_target * 0.2 > 20 credits, gate on AskUserQuestion unless the user pre-authorized. This is the cost-burn protection.
What you get back
Per place: name, address, phone, website, category, rating, review_count, hours, place_id, latitude/longitude, sometimes email if surfaced.
Phase 2 — quality filter (free, in-memory)
Most local-prospect campaigns waste budget on the long tail. Apply local filters BEFORE enrichment:
| Filter | Why |
|---|
rating >= 4.0 AND review_count >= 10 | Excludes brand-new/low-traction businesses unless those are the target |
rating < 3.5 | Inverse — find unhappy local businesses (fertile ground for "we can fix your reviews" pitches) |
website == null | Find businesses without web presence (web-design / SEO agency target) |
phone == null | Excludes shell listings |
Dedupe on phone AND address | Multi-location chains list each branch as a separate place |
Always say which filters you applied and how many places remained. Don't silently drop rows.
Phase 3 — normalize (free / cheap)
Before enrichment:
- Phone: pass each
phone through normalize_phone to E.164 format (~0.5 credits batched, but check catalog for current price). Required for SMS/dialer downstream.
- Domain: extract clean domain from
website via clean_domain (free local function). Strips www., query strings, fragments.
- Owner-name guess: if the place name contains a person ("Dr. Sarah Chen DDS"), split into first/last for use in Phase 4 email finding. If it's a brand name, skip.
Phase 4 — enrich (opt-in, costed)
ONLY run the enrichments the user actually asked for. Each is independent and costed:
4a. Website-less businesses → find_website_by_company_name (0.5/call)
For places where website == null but the business has a real name. ~50% hit rate; cap to top-N candidates to control cost.
4b. Owner / decision-maker email
Two paths, chained narrowest-first:
- From the website →
web_emails (cheap, scrapes contact pages). Often surfaces info@, sometimes the owner's direct address.
- From owner name + domain (if Phase 3 split a person name out) →
email_finder (≤2 credits/success, waterfall). Use ONLY when web_emails returned nothing actionable.
Skip personal-email tools (find_personal_email) unless the user explicitly opted in — most local outreach uses the business address, not personal email.
4c. Tech stack → web_tech_stack (1/call)
Run only when the prospecting hook depends on tech (e.g. "find restaurants on Toast", "agencies running WordPress"). Otherwise skip — adds noise.
4d. Review mining (VoC) → google_maps_reviews_scraper (0.05/review)
Powerful but easy to over-spend on. Caps:
- Default: pull
max_reviews: 25 per place — enough for a sentiment summary.
- Always set
sort: "lowest" first — negative reviews are the gold for "we can fix this" outreach hooks. Add a second pass with sort: "highest" only if you need a balanced view.
- Never call review scraping on the full place list silently — that's how you burn 5,000 credits in one call. Cap to top-N by relevance and confirm.
Per place, extract:
- 3 most-cited pain themes (e.g. "long wait", "billing surprises")
- 1 standout positive (for credible flattery in outreach)
- Owner-reply behavior (responsive owners are more receptive to vendor pitches)
Phase 5 — assemble the deliverable
Output a single CSV-ready table the user can hand to crm-export. Column standard:
place_name, category, address, city, state, zip, phone_e164, website, domain,
rating, review_count, hours_summary, owner_name_guess, owner_email,
tech_signals, top_pain_themes, outreach_hook, source_place_id, source_url
Always include source_place_id and source_url so each row is traceable back to its Maps listing. Skip rows that failed Phase 2 — don't pad the deliverable with junk.
Cost ceiling
This skill burns budget fast on review mining. Hard caps before any phase begins:
| Operation | Default cap | Hard cap (require AskUserQuestion above) |
|---|
| Place sourcing | 50 places | 200 places (40 credits) |
| Review mining | 25 reviews × 10 places (12.5 credits) | 100 reviews × 50 places (250 credits — needs explicit OK) |
| Website lookup | top 30 of website-less | top 100 |
| Email finder | top 30 with owner names | top 100 |
State the running cost estimate after each phase. If the total trends above 50 credits, pause and confirm.
Anti-patterns
- ❌ Calling
google_maps_reviews_scraper on every place in the list. Always cap and explain.
- ❌ Mixing this skill with
build-prospect-list in the same query. Source first here, then escalate.
- ❌ Sending personal email outreach to local businesses. Use the business contact (Phase 4a/4b).
- ❌ Skipping the dedupe step — chains will inflate your list with phantom variety.
- ❌ Treating low-rating places as low-quality leads when the campaign is "fix your reviews" — they're the target, not noise.
Follow-ups to suggest
- →
enrich-and-verify to verify any owner emails before sending.
- →
evidence-score if the user wants leads ranked (review-trend slope, web-presence gap, tech mismatch).
- →
sequence-builder (SMS-friendly) for outbound. Local SMB hates long emails — keep it 2-3 lines.
- →
crm-export for HubSpot/Pipedrive/GHL handoff.
Offer to schedule this
Local prospecting has high recurring value when the campaign is geographic expansion ("every new city we enter, source the top 50"). At session end, offer:
"Want me to run this for the next city (or weekly, for new openings) on a schedule? → scheduled-workflow"
One offer, then drop it if declined.
