I need a reliable scraping solution that can pull complete product catalogues from a handful of e-commerce websites (links provided once we start) and return everything in Shopify’s native bulk-upload format. That means every row you deliver must already match Shopify’s column structure—Handle, Title, Body (HTML), Vendor, Type, Tags, Option sets, Price, Compare-at price, Image Src, Inventory levels and any other fields the platform expects—so I can drop the CSV straight into the store without extra cleanup. Scope of data • Product details: titles, full descriptions, variants, SKU/handle consistency, images, tags, inventory. • Pricing information: current selling price and compare-at price for each variant where available. Important notes • The scrape has to be automated (Python, Scrapy, BeautifulSoup or similar) and repeatable; if you can hand over the script as well, even better. • Accuracy counts: no duplicate handles, no broken image links, and all prices must match what’s live on the source sites at the moment of scraping. • Products with multiple variants should appear on consecutive rows exactly as Shopify expects. • Image URLs must be absolute; if you prefer to download images, place them in a structured folder and reference them correctly in the CSV. Deliverables 1. Shopify-ready CSV containing all required columns and populated rows for each product/variant. 2. Image assets or verified image URLs. 3. (Optional but appreciated) the scraping script with brief usage notes. Let me know your approach, the tools you plan to use, and an estimated turnaround once the target sites are confirmed.