I need a clean, well-structured extract of text-based product listings from a specific website, delivered fast. The task is straightforward: crawl the site, capture every relevant field that appears on each listing (titles, descriptions, prices, SKU codes, category paths and any other on-page text detail), then supply the data back to me in a spreadsheet-ready format such as CSV or XLSX. I do not need images—only the textual content. Any solution you craft must respect the site’s robots.txt and avoid rate-limit issues so nothing gets blocked. A Python script using requests/BeautifulSoup, Scrapy, or a headless browser (Selenium, Playwright) is perfectly fine as long as your code is clearly commented and reusable; if you prefer another language, that works too provided the result meets the same standards. Deliverables: • Complete dataset of all product listings in CSV or XLSX • The scraping script with brief run instructions • One short report summarising page coverage and any skipped items Acceptance criteria: the dataset must align 100 % with on-site values at the time of scrape, contain no duplicate rows, and load without errors in Excel or Google Sheets. Turnaround is tight, so let me know how quickly you can run the first pass once access details are shared.