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:

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

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

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:

0.2 Keyword Auto-Adjustment Rules

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

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:

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│   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:

Keyword Swap Table (auto-applied):

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:

[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:

Auto-chain detection (no manual list needed):

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"

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

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:

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:

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

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.

Step 7: Output (3-layer structure)

Save to prospect-data/{batch}/:

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):

{
  "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):

{
  "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:

{
  "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:

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):

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:

"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.

End of SKILL.md