Lead Extractor

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name: lead-extractor description: "Extract structured real-estate lead records from parsed message objects. Use when users ask to find leads in WhatsApp exports, extract name-phone-budget, or classify listing vs requirement posts. Recommended chain: run after message-parser and before india-location-normalizer. Do not use for storage, summaries, outbound messaging, or action execution."

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Lead Extractor

Identify lead signals in parsed messages and emit strict lead objects.

Quick Triggers

Find all buyer leads from this WhatsApp chat.

Extract contact details and budget from these messages.

Identify serious property inquiries from parsed messages.

Recommended Chain

message-parser -> lead-extractor -> india-location-normalizer

Execute Workflow

1. Accept parsed messages from Supervisor. 2. Validate input with references/parsed-message-input.schema.json. 3. Apply chat-specific extraction rules from references/extraction-rules-re-india-v1.md. 4. Determine dataset_mode from Supervisor context: - default: broker_group - allowed: broker_group, buyer_inquiry, mixed 5. Detect lead-candidate messages using inquiry intent, contact details, and property-related preferences. 6. Classify record_type: - inventory_listing for broker inventory/availability posts (default in broker groups) - buyer_requirement for explicit "required/chahiye looking for" demand posts - drop non-lead/system noise instead of emitting noise_or_system 7. Handle multiline listings as one candidate record when body lines contain price, area, or location details. 8. Build lead records with: - required: lead_id, name, phone, record_type - optional: dataset_mode, property_type, budget, deal_type, asset_class, price_basis, area_sqft, area_basis, location_hint, raw_text, source, created_at 9. Normalize phone extraction from spaced variants such as +91 98205 82462 and 98200 78845. 10. Distinguish price intent from rate intent: - examples: 3.5 Lakh rent (monthly), 60K psf (per-sqft), 4.25 Cr (total) 11. Deduplicate leads by stable keys when records clearly refer to the same person. 12. Validate output with references/output-leads.schema.json. 13. Return only validated lead objects.

Enforce Boundaries

Never write or update persistent storage.

Never modify source messages.

Never generate summaries.

Never suggest or execute follow-up actions.

Never send communication or invoke external side effects.

Handle Errors

1. Reject invalid parsed-message input. 2. Emit an empty array when no lead evidence exists. 3. Return field-level validation errors when extracted records violate schema.