I need a clean, repeatable scraper that pulls comprehensive product details directly from Sephora’s website. The data set has to cover far more than the basics: along with live price and stock status I also want the full product description, brand name, SKU code, pack or size information, and any promotional wording that appears on the page (discount banners, “limited-edition” tags, gift-with-purchase notes, etc.). Because this project centres on Sephora only, you can tune your approach to whatever will stay stable against their layout changes—Python with BeautifulSoup or Scrapy, Node with Puppeteer, or another stack you’re comfortable with—as long as the final script is documented and can be rerun on my side without a steep learning curve. Deliverables • A well-annotated script or notebook that scrapes the fields listed above from every product URL in a category I specify. • Output saved to CSV or JSON with consistent column names. • Brief read-me explaining setup, required libraries, and how to schedule or batch-run the scraper. • One test run that proves price and availability are captured in real time. I’ll provide sample categories once we kick off, and I’ll test the script on my machine before sign-off. If you’ve handled high-volume e-commerce scraping before, especially where anti-bot measures are in play, let me know—speed and resilience matter more than flashy dashboards here.