E-commerce Multi-Platform Product Data Analysis Assistant

The problem it addresses

When a user has a product link from JD, Taobao, Pinduoduo, Douyin, or 1688, the next step is usually manual: open other platforms, copy keywords, search comparable listings, then compare price, rating, review count, and shop information. This skill turns that workflow into a structured process: parse the platform from the link, extract the source product details, search comparable listings across platforms, and generate a comparable HTML report. It is better suited for engineers, procurement, operations, or comparison workflows that need cleaned-up data, rather than a full production crawler.

Core workflow and limits

• Link parsing: recognizes domains such as jd.com, taobao.com, pinduoduo.com, douyin.com, and 1688.com; short links may need to be expanded or converted into keywords first.
• Detail extraction: uses WebFetch to retrieve title, price, specs, rating, review count, and shop information; if blocked, it falls back to WebSearch with the product title.
• Cross-platform search: searches Taobao, JD, Pinduoduo, Douyin, 1688, and Suning using core product terms, model names, or keywords, then extracts the top 3 relevant listings per platform with price and rating.
• Comparison analysis: calculates lowest price, highest price, average price, price gap percentage, highest-rated platform, and a value score based on price, rating, and sales data when available.
• Report output: saves structured JSON to /tmp/ecommerce_data.json, then runs scripts/generate_report.py to create an HTML report with charts and purchase recommendations.

Because e-commerce sites have strong anti-scraping behavior, data may be missing, incomplete, or time-sensitive. At least three platforms should be covered for a meaningful comparison; if only one platform is found, manual supplementation is recommended.