Automate the transformation of unstructured chat messages into structured real estate lead records with precision.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install lead-extractor
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install lead-extractor using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Lead Extractor?
The Lead Extractor is a robust technical solution within the Openclaw Skills ecosystem designed to parse and structure real estate data from message objects. It excels at identifying intent in messy communication channels like WhatsApp exports, converting fragmented text into actionable lead objects containing names, contact details, and financial requirements. This skill is essential for developers looking to automate the first mile of real estate CRM data entry.
By leveraging specialized extraction rules, this tool distinguishes between property listings and buyer inquiries, ensuring that Openclaw Skills can handle complex broker group dynamics. It focuses purely on data extraction and normalization, maintaining a clean separation of concerns by avoiding direct storage or communication tasks.
Lead Extractor Use Cases
- Identifying buyer requirements and budget constraints from WhatsApp export files.
- Automating the classification of broker inventory posts into structured listing records.
- Extracting contact information and property preferences from high-volume chat logs.
- Detecting serious property inquiries within mixed-intent communication streams.
How Lead Extractor Works
- Receives parsed message input from a supervisor agent or a preceding parser skill.
- Validates the incoming data structure against predefined message schemas.
- Applies real estate specific extraction rules to identify buyer signals or inventory posts.
- Determines the dataset mode to differentiate between broker groups and direct buyer inquiries.
- Normalizes phone numbers from various formats and extracts financial intents like total price or rent.
- Deduplicates records based on stable keys to prevent redundant data entries.
- Returns a validated array of lead objects for further processing by other Openclaw Skills.
Lead Extractor Setup
To integrate this capability into your workflow, ensure your environment is configured to handle the standard Openclaw Skills data exchange format. Install the necessary references and execute the following command to initialize the component:
npm install @openclaw/lead-extractor-skill
You must also provide the extraction-rules-re-india-v1.md and output-leads.schema.json files in your project's reference directory for proper validation.
Lead Extractor Data Schema & Taxonomy
The Lead Extractor skill organizes data into a strict schema to ensure compatibility across the Openclaw Skills framework.
| Field | Type | Description |
|---|---|---|
| lead_id | string | A unique identifier generated for the specific record. |
| record_type | enum | Classified as either inventory_listing or buyer_requirement. |
| phone | string | Normalized contact number (e.g., +91XXXXXXXXXX). |
| budget | string | Extracted price, rent, or per-sqft rate details. |
| area_sqft | number | The property size converted to square footage. |
| location_hint | string | Raw location text for downstream normalization. |
Lead Extractor Advanced Features
- Context-aware dataset modes for handling broker-only or mixed-user groups.
- Multiline listing detection to capture comprehensive property details across multiple chat lines.
- Intelligent financial intent detection to distinguish between total asset value and monthly rent.
- Built-in deduplication logic to maintain data hygiene within the Openclaw Skills pipeline.
SKILL.md
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