Prospecting — B2B Lead Generation from Existing Customers#

Overview#

Turn existing customers into a search template → find similar businesses on Google Maps → enrich → score → output actionable call lists.

One line: Known customer → profile → Maps search → enrich & rank → CSV call list + JSON index

Compliance & Legal Boundaries#

Before running this skill, confirm:

ItemRequirementGeographyOnly collect businesses in jurisdictions where B2B outreach is lawfulData source termsComply with Google Maps Terms of Service, Yelp ToS, and any site scrapedRate limitingAdd reasonable delays between requests; do not hammer the same siteDo-not-call / anti-spamRespect opt-out lists and local telemarketing rulesPrivacy lawsGDPR / CCPA / PIPL: avoid collecting unnecessary personal contact infoData accuracyOnly use publicly listed business contact info; do not enrich personal mobile numbers or home addresses

This skill collects publicly visible business information only. Do not use it to harvest personal emails, mobile phones, home addresses, or non-business contacts.

Data Retention & Storage#

RuleImplementationOutput pathprospect-data/{batch}/ under the user's workspaceRetentionDelete or archive prospect files when the campaign ends or they are no longer activeSensitive dataDo not store personal mobile numbers or non-business contactsSharingDo not share raw JSON/CSV with third parties without consentEncryptionMove completed batches to encrypted storage if required by your policyAccessTreat call lists and profile JSON as internal sales data; restrict access to authorized users

When to Use#

• User gives a customer name + location and asks to find similar businesses
• User asks to build a prospect/call list
• User wants to find new clients in a specific industry (auto body, manufacturing, HVAC, etc.)

Input Required#

FieldRequiredNotesCompany name✅Core search termLocation (city/state)✅Search center pointProduct purchased❌Helps with profiling

Even minimal input ("Bob's Auto Body, Orange CA") can start the full flow.

Execution Flow#

Step 0: Self-Diagnostic & Adaptive Search Design (NEW — Auto-Optimization)#

Before executing searches, the skill automatically analyzes search parameters and adjusts strategy without user intervention:

0.1 Coverage Gap Detection

After initial search round completes, automatically check for these warning signals:

Warning SignalThresholdAuto-ActionZero results for keyword+center0 listings returnedSwap keyword (e.g., "paint shop" → "auto paint" / "car paint" / "collision")Low unique yield per center<5 unique businesses per center after dedupExpand radius or add satellite centerHigh non-target ratio>30% listings are wrong industryTighten keyword (e.g., "paint shop" → "auto body paint" / "collision paint")Chain under-representation0 chain brands in resultsAdd brand keywords (Caliber, CARSTAR, Maaco, Gerber)Equipment-related missingNo spray booth / frame machine in resultsAdd equipment keywords to second pass

0.2 Keyword Auto-Adjustment Rules

text

IF center + "paint shop" returns <5 valid auto-body listings:
  → REPLACE with "auto paint shop" OR "car paint" OR "collision paint"

IF center + "auto body shop" returns 0 results:
  → TRY "body shop" OR "collision repair" OR "auto repair"

IF chain brands known in industry but missing from results:
  → ADD "[brand] + city" as explicit search (e.g., "Caliber Houston")

0.3 Multi-Pass Search Protocol

text

PASS 1: Core keywords (auto body shop, collision repair, paint shop)
  ↓
Auto-analyze coverage gaps
  ↓
PASS 2: Gap-fill keywords (adjusted based on PASS 1 results)
  ↓
PASS 3: Equipment/brand keywords (spray booth, frame machine, Caliber, CARSTAR)
  ↓
Final dedup + scoring

No user input required — the skill self-diagnoses and adjusts between passes.

Step 1: Profile the Existing Customer (8-step fixed process)#

Read references/profiling.md for the full 8-step process. Key actions:

1. Google Maps deep dive — Use agent-browser to search [company name] [location], extract: address, phone, rating, review count, business type, hours, website, photos, chain status
2. Review sampling — Sample reviews with keyword filtering (not all reviews). Generic keywords: new, expand, equipment, upgrade, install, moved, bigger + industry-specific keywords (e.g., for auto body: paint booth, insurance, fleet, dealer)
3. Social/web enrichment — Only for 🔴 chain (FB+LinkedIn+website) or 🟡 mid-tier (FB+website). Skip 🟢 small (no website)
4. Output a Profile Card — Standard format saved to prospect-data/{batch}/profile-{name}.json

Tier detection (determines enrichment depth):

• 🔴 Chain/large: name contains chain markers OR >200 reviews
• 🟡 Mid-tier: has website, 50-200 reviews
• 🟢 Small: no website, <50 reviews

Step 2: Maps Batch Search (agent-browser automated, with self-optimization)#

Read references/search-strategy.md for the complete search framework.

NEW: Self-Optimizing Search Loop

The search now runs in 3 automatic passes with gap detection between each:

text

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   PASS 1        │     │   PASS 2        │     │   PASS 3        │
│   Core keywords │────→│   Gap-fill      │────→│   Equipment/    │
│   (4-6 per      │     │   (auto-adjusted│     │   Brand deep    │
│    center)      │     │   based on P1)  │     │   dive          │
└─────────────────┘     └─────────────────┘     └─────────────────┘
       ↓                       ↓                       ↓
   [Auto-analyze]          [Auto-analyze]           [Final dedup]
   Coverage gaps            Remaining gaps           + Score

Pass 1: Core Search (always runs)

• Multi-center: Large cities (>2M) use 4-6 search centers
• Keyword matrix: 4-6 keywords per center (core + service + equipment + brand + scene)
• Pagination: Scroll and load 3 times per search
• Deduplication: Cross-center, cross-keyword deduplication

Pass 2: Gap-Fill (auto-triggered if gaps detected)

After Pass 1 completes, automatically check:

CheckConditionAuto-ActionZero-result centersAny center returned 0 listings for >2 keywordsAdd 1-2 satellite centers (midpoint between existing centers)Low-yield keywordsAny keyword returned <3 valid listings across all centersReplace with synonym (see Keyword Swap Table below)Missing chainsKnown chain brands not foundAdd explicit brand+city searchesGeographic holesLarge gaps between centersAdd midpoint center

Keyword Swap Table (auto-applied):

Original (low yield)Replacement 1Replacement 2Replacement 3paint shopauto paint shopcar paintcollision paintauto body shopbody shopcollision repairauto repaircollision centercollision repairauto body repaircrash repairframe machineauto framechassis repairstructural repairspray boothpaint boothauto paint boothHVLP booth

Pass 3: Equipment & Brand Deep Dive (auto-triggered)

If target industry involves equipment (auto body, manufacturing, HVAC), automatically add:

• Equipment keywords: spray booth, frame machine, car lift, CNC mill, etc.
• Brand searches: [Brand] + [City] for known chains (Caliber, CARSTAR, Maaco, Gerber, Crash Champions)

Search execution:

1. For each center point × each keyword: open Google Maps, extract listings, paginate 3x
2. Collect: name, phone, address, rating, review count, business type, website status, chain markers
3. Dedup: same name + same address = duplicate
4. Remove: permanently closed, non-target industry

Save to: prospect-data/{batch}/candidates-raw.txt (raw extraction log) + candidates.json (deduplicated)

Auto-optimization logging: After each pass, append to candidates-raw.txt:

text

[PASS-N-ANALYSIS] Center: X, Keyword: Y, Results: Z, Action: [none|swapped|expanded|added-brand]

Step 3: Auto-Tier Candidates (with self-validation)#

Based on Maps data, assign tiers. Chain stores are NOT excluded — they are valid prospects with a different approach strategy.

NEW: Self-Validation Checklist (auto-executed before tiering)

Before finalizing candidates, automatically verify:

CheckAction if FailedDuplicate names with different addressesMark as chain locations, keep allSame name + same address appearing multiple timesDeduplicate, keep most completeBusiness type mismatch (e.g., "car wash" labeled as "collision")Re-classify or flag for manual reviewPhone placeholder pattern (555, 000, 1234)Flag as phone_status: unverified_placeholderAddress in different city than search centerVerify: satellite location vs data error

Auto-chain detection (no manual list needed):

text

IF name contains: Caliber, CARSTAR, Maaco, Gerber, Crash Champions, ProColor, 1st Choice
  → Mark as chain_brand: [detected brand]

IF same name appears in >2 different addresses across centers
  → Mark as chain_brand: [normalized name]
  → Set tier: "连锁-XX"

TierCriteriaNext action🔴 Chain/largeChain name OR >200 reviews OR multi-location detectedDeep enrichment + chain procurement strategy🟡 Mid-tierHas website, 50-200 reviewsMedium enrichment🟢 SmallNo website, <50 reviewsSkip enrichment

Chain store prospecting strategy — Read references/chain-strategy.md for the full three-call approach:

• Call 1: Local store — NOT to sell, but to identify procurement decision chain
• Call 2: Regional/corporate — pitch to the person who can approve multi-location deals
• Call 3: Follow-up with proposal

Key principles:

• Chain stores have large, stable equipment needs — one deal can cover multiple locations
• Local store manager is the entry point, not the decision-maker (usually)
• Key question: "Is equipment purchasing handled locally, or should I speak with your regional/corporate procurement team?"

Step 4: Enrich by Tier#

TierActionToolsTime🔴 ChainWebsite deep + LinkedIn + news search + chain procurement mappingagent-browser + agent-reach (Exa)3-5min each🟡 MidWebsite basics + FBagent-browser1-2min each🟢 SmallSkip — Maps data sufficient—0

Chain enrichment with agent-browser:

1. agent-browser open "[website URL]"
2. agent-browser snapshot -i → extract Services, About, Staff, Contact
3. Check for Portfolio/Cases and News/Blog pages for expansion signals
4. For chains: Look for corporate/region procurement contacts, preferred vendor programs, and expansion news

Chain news search with agent-reach:

text

mcporter call 'exa.web_search_exa(query: "[company name] expansion OR new location OR equipment", numResults: 5)'

Chain procurement mapping (chains only) — See references/chain-strategy.md for full approach:

• Identify: local manager → regional operations manager → VP of operations / procurement director
• Sources: LinkedIn, corporate website "careers" or "partners" page, news about leadership changes
• Goal: find the person who can approve equipment purchases for multiple locations

Step 5: Score & Rank#

Match each candidate against the profile card:

FactorRulePointsBuy signalExpansion / new service / new equipment+5 (strong) / +3 (medium) / +1 (weak)Industry matchBusiness type matches profile+3Scale matchReview count / bays similar to profile+2Service overlapSame services as profile+2Geo similaritySimilar area type+1Business ageSimilar years in operation+1Chain multiplierChain store (multiple locations = bulk potential)+3EV/high-end certificationEV Certified / LUXE / premium line+4

Tie-breaking: buy signal strength → chain (bulk potential) → has phone → closer scale match

Total scorePriorityAction10+🔴 HighCall within 48h6-9🟡 MediumCall this week<5🟢 LowCall when available

Step 6: Generate Custom Sales Openers#

Not templates — custom for each prospect based on their data.

Opener must accomplish 3 things: (1) prove you know them, (2) state your purpose, (3) invite dialogue.

Data sourceHow to use in openerBuy signal"Saw you just added [service related to your product]"Similar customer"We supplied [product] to [similar customer] in your area"Business type"Since you do [their business type]..."Key clues"As an [industry certification] shop..." / "Working with [their key client]..."TierHigh→emphasize quality & custom, Mid→value, Low→entry-levelChain store****Key opener question: "Is equipment purchasing handled locally, or should I speak with your regional/corporate procurement team?"Premium/certified lineReference their specialization: "As an EV-certified shop, you need [specific configuration] — we've done those."

Step 7: Output (3-layer structure)#

Save to prospect-data/{batch}/:

text

prospect-data/{area}-{date}/
├── index.json          ← Lightweight index, instant search
├── P001.json           ← Full detail for first prospect
├── P002.json           ← Full detail for next prospect
└── call-list.csv       ← 11-column CSV for calling

See examples/ for sample output files.

Then export CSV from index + P###.json files for calling.

index.json — Search/filter only (few KB):

json

{
  "batch_id": "orange-ca-2026-05-19",
  "source_customer": "ABC Auto Body",
  "generated": "2026-05-19",
  "search_areas": ["Orange CA"],
  "product": "Customizable per industry",
  "chain_strategy": "Chain stores included — call local first to identify procurement decision chain, then escalate to regional/corporate",
  "prospects": {
    "P001": {
      "name": "Bob's Auto Body",
      "city": "Orange CA",
      "priority": "高",
      "tier": "中高端-独立",
      "status": "待联系",
      "tags": ["[industry]", "[business type]"],
      "file": "P001.json"
    },
    "P013": {
      "name": "Crash Champions Orange",
      "city": "Orange CA",
      "priority": "高",
      "tier": "连锁-中高端",
      "status": "待联系",
      "tags": ["collision", "chain", "Crash Champions"],
      "file": "P013.json"
    }
  }
}

P001.json — Full detail (all collected data + contact log):

json

{
  "id": "P001",
  "name": "Bob's Auto Body",
  "phone": "[EXTRACTED_FROM_MAPS]",
  "city": "Orange CA",
  "tier": "Mid-high-Independent",
  "priority": "High",
  "buy_signal": "Added new [service]",
  "similar_customer": "Customer A",
  "business_type": "[industry service type]",
  "key_clues": "[specific observations from data]",
  "email": "bob@bobscorp.com",
  "chain_brand": null,
  "opener": "We supplied [product] to [similar customer] in your area — saw you recently added [service]. What [product type] are you currently using?",
  "status": "Pending",
  "contact_log": [],
  "tags": ["[industry]", "[business type]", "[certification]"],
  "maps_url": "https://maps.google.com/...",
  "rating": 4.5,
  "reviews_count": 87,
  "has_website": true,
  "website_url": "https://bobscorp.com",
  "raw_notes": "Reviews mention...",
  "source_customer": "Customer A"
}

P013.json — Chain store example:

json

{
  "id": "P013",
  "name": "[Chain Brand] [City]",
  "phone": "[EXTRACTED_FROM_MAPS]",
  "city": "Orange CA",
  "tier": "Chain-Mid-high",
  "priority": "High",
  "buy_signal": "National chain with stable equipment needs across locations",
  "similar_customer": "Customer A",
  "business_type": "[Industry] Chain",
  "key_clues": "[Chain brand] national chain + [city] location + online booking",
  "email": "",
  "chain_brand": "[Chain Brand]",
  "opener": "Hi, I'm with [company] — we manufacture [product]. [Chain brand] has a location here, and I'd like to learn about your equipment purchasing process. Is that handled locally, or should I speak with your regional/corporate procurement team?",
  "status": "Pending",
  "contact_log": [],
  "tags": ["[industry]", "chain", "[chain brand]", "online booking"],
  "maps_url": "https://maps.google.com/...",
  "rating": 4.6,
  "reviews_count": 120,
  "has_website": true,
  "website_url": "https://www.chainbrand.com",
  "raw_notes": "National chain. Key question: local manager vs regional purchasing.",
  "source_customer": "Customer A"
}

CSV export — 11 columns, ready to call:

text

优先级,店名,电话,城市,档位,购买信号,相似客户,业务类型,关键线索,邮箱,开场白

CSV columns map 1:1 to P###.json fields (priority→tier, etc.). CSV is a projection of the JSON, not a separate data source.

Status tracking (in P###.json, not CSV):

text

待联系 → 已联系 → 意向 / 无意向 / 回访中
                 ↘ 无人接听 → 再试

Step 8: Update contact status#

When user reports call results, update P###.json:

json

"contact_log": [
  {"date": "2026-05-20", "action": "电话", "result": "无人接听", "next": "明后天再试"}
]

And update index.json status field accordingly.

Re-export CSV filtered by status when user needs a new call list.

Critical Rules#

1. Every step must execute — skip only if data source has nothing (no website = skip website enrichment)
2. Review sampling, not all — use tiered sampling + keyword filtering per profiling reference
3. Social media by tier only — 🔴 chain gets full search, 🟢 small gets nothing
4. Opener is custom — never use generic templates, always tailor to prospect's specific data
5. Output is 3-layer — index.json for search, P###.json for detail, CSV for calling
6. CSV is a projection — all data lives in JSON; CSV is just 11 columns exported on demand
7. Chain stores ARE valid prospects — do NOT exclude them. Include with a different strategy: local call first → identify procurement decision chain → escalate to regional/corporate buyer. One chain deal can equal many independent deals.
8. Tier labels include chain distinction — use "独立" (independent) or "连锁" (chain) suffix in tier: e.g., "中高端-独立", "连锁-中高端"
9. Chain opener must ask about procurement — "Is equipment purchasing handled locally, or should I speak with your regional/corporate procurement team?"
10. Specialized/certified prospects are high priority — certifications (EV, ISO, specific industry standards) indicate higher equipment requirements and justify premium positioning
11. DATA INTEGRITY — NO FABRICATION — All data in outputs MUST come from actual agent-browser searches, web_fetch calls, or other real data sources. NEVER invent, infer, or hallucinate business details. If a field cannot be verified from real data, mark it as "unknown", "not found", or "pending verification". If a search returns no results or fails due to network issues, report this honestly to the user instead of generating placeholder data.
12. TRANSPARENCY ON DATA GAPS — If Google Maps returns restricted view (limited details), if agent-browser fails to load, or if a business has no visible phone/address/rating, document this in raw_notes and adjust the priority accordingly. Do not fill gaps with assumptions.
13. VERIFICATION REQUIRED — Before marking any prospect as "ready to call", confirm that the phone number was actually extracted from a live page (not a template). If the number is a placeholder or unverified, flag it explicitly: "phone_status": "unverified_placeholder".
14. PLACEHOLDER PHONE POLICY — Output phone numbers must come from real page extraction. Any 555, 0000, 1234, or similar placeholder pattern must be marked "phone_status": "unverified_placeholder" and must not be written to the call-list.csv dial column.
15. OUTREACH BOUNDARY — The generated call list is for business-to-business contact only. Honor "do not call" requests. Stop calling a prospect upon request and mark status as "do_not_contact". Identify caller, company, and purpose on every call.
16. DATA RETENTION — Delete or archive prospect files when the campaign ends. Do not share raw JSON/CSV with third parties without consent.
17. SELF-OPTIMIZATION IS MANDATORY — The skill MUST automatically detect coverage gaps and adjust keywords/centers between search passes. Do NOT require user feedback to fix zero-result keywords or missing chain brands. Document all auto-adjustments in candidates-raw.txt with [PASS-N-ANALYSIS] markers.
18. COVERAGE GAP REPORTING — After all passes complete, generate a coverage-report.json documenting: (a) which center+keyword combinations returned zero results, (b) which auto-adjustments were applied, (c) estimated coverage percentage of target businesses in the area. This helps users assess completeness without manual review.
19. SUBURBAN EXPANSION IS AUTOMATIC — For cities >2M population, if initial 4-6 centers yield <50 unique prospects, auto-expand to 8-10 centers covering satellite cities. For Houston-sized markets, target 80-100+ prospects before stopping. Do NOT require user to ask for "more coverage" — the skill should self-assess and expand.
20. FILTER CALIBRATION IS REQUIRED — After first dedup, if >20% of valid prospects were filtered out (e.g., chain brands, legitimate shops with unconventional names), auto-relax filter rules and re-run. Log filter false-positives in candidates-raw.txt.
21. CHAIN BRAND LEARNING — Maintain a dynamic chain-brands-detected.json per search session. If a brand appears in >2 locations, add it to the known chain list for future searches. Share learnings across sessions via MEMORY.md updates.

Version History#

• v2.0.0 (2026-05-23): Added self-optimization protocol, coverage gap reporting, chain brand learning, suburban expansion rules, and filter calibration. Houston field test validated 90 prospects from 11 centers with 10 auto-adjustments.
• v1.0.0 (2026-05-19): Initial release with multi-center search, chain strategy, 3-layer output, and custom sales openers.

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End of SKILL.md