About this skill

Problem

Sales leads are often scattered across public sources such as corporate databases, B2B directories, social platforms, and trade-show rosters. Fields are inconsistent, duplicates are common, and records may lack industry, region, company size, or intent structure. Manual processing is slow and can drift into non-public data or excessive requests.

How the skill works

lead-scraper turns public lead sources into a structured pipeline:

Multi-platform scraping: covers corporate information platforms, B2B platforms, social platforms, and industry resources.
Data cleaning: normalizes formats, filters invalid records, and removes duplicates.
Smart classification: tags leads by industry, region, size, and intent level.
Lead scoring: estimates lead quality and priority for follow-up ranking.
Multi-format export: outputs Excel, CSV, and JSON, or pushes to a CRM.

A typical flow is selecting sources, extracting public fields, cleaning and deduplicating, classifying and scoring, then exporting or syncing to CRM.

Boundaries and cautions

This skill is intended for public-information lead enrichment, not for bypassing CAPTCHAs, accessing non-public data, or malicious high-frequency requests. Confirm target platform terms, respect robots.txt, privacy settings, PIPL, and data-security rules. Obtain consent from data subjects before marketing.

Screenshots

Use Cases

Sales teams collect public leads from Qichacha, 1688, and LinkedIn, then build a follow-up sheet tagged by industry, region, and intent.
Operations staff clean and deduplicate trade-show rosters, association members, and B2B data, then export CSV for CRM import.
Marketing analysts classify public company leads by industry, region, and size, then shortlist high-priority accounts by score.
Sales managers normalize scattered potential-customer records and sync them to CRM as an assignable task queue.