Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Align broker-group contracts: dataset_mode, record_type, and expanded summary/prioritization schemas.
What This Skill Does
Extracts structured lead records from parsed WhatsApp real-estate messages. Classifies each message as an inventory listing or buyer requirement, and pulls name, phone, budget, and property details into a validated schema. Designed to run after message-parser and before india-location-normalizer.
Handles India-specific number formats, price notation (Lakh, Cr, psf), and real estate terminology that generic extractors misparse or ignore.
When to use it
- Finding all buyer inquiries in a WhatsApp broker group export
- Pulling contact details and budgets from real estate chat dumps
- Separating listing posts from demand posts in mixed group chats
- Parsing spaced or country-code phone numbers from informal messages
- Deduplicating repeat contacts across a bulk message dataset
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Extract all leads from this WhatsApp broker group export
- 1Validates parsed message input against the parsed-message-input schema
- 2Applies India real estate extraction rules to detect lead-candidate messages
- 3Classifies each candidate as inventory_listing or buyer_requirement
- 4Builds lead records with name, phone, budget, location, and deal type fields
- 5Validates output records against the output-leads schema and deduplicates by stable key
Array of validated lead objects with structured fields including lead_id, name, phone, record_type, budget, and location_hint