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What it does
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.
Skill profile
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More options in Sales Outreach.
Claude Code · Codex · OpenClaw
Python
Updated 6/16/2026
Agent compatibility
Compatibility has not been reviewed for this listing yet. Check the publisher documentation before installing.
Installation
npx skills add https://github.com/modbender/skill-library-mcp --skill lead-extractorReview source code and installation permissions before adding third-party tools to an agent.
lead-extractor is organized in the Sales Outreach category. Compare its source, install method, and compatibility before adding it to your workflow.
Third-party agent tools may access source code, credentials, or browser sessions. Read the source documentation and use the minimum permissions needed.
npx skills add https://github.com/modbender/skill-library-mcp --skill lead-extractorSKILL.md
--- 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." --- # 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.
skill
ECNU-ICALK
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