1688-商品热销榜单
Query 1688's best-selling product rankings to assist with product research and selection.
Install & Use
Copy this prompt and send it to your AI assistant (Claude / Cursor / TRAE / Codex / WorkBuddy etc.) to auto-install:
Help me install this AI Skill: 1688-商品热销榜单. It is used for: Query 1688's best-selling product rankings to assist with product research and selection. Full Skill content: https://321skill.com/skills/1688-hot-sales-ranking-3/raw/index.md Read that page and install it.
The prompt includes a link to the full Skill content. You can also view the full content.
This Skill addresses the challenge of quickly obtaining best-selling product data when sourcing and selecting wholesale products on the 1688 platform. Traditional manual browsing of stores and product listings is inefficient and lacks systematic comparison of weekly or monthly trend rankings, making it easy to miss out on trending products. With this Skill, you can directly query 1688's official best-selling product rankings to obtain structured hot product data, providing data support for product selection decisions.
Usage is very simple—you just need to describe your requirements in natural language. For example, specify parameters such as keywords, ranking type (weekly/monthly), date, sorting method (by order count, sales volume, sales amount, etc.), price range, and the Skill will automatically call the 1688 ranking API and return results. It supports paginated browsing and various filtering conditions, such as precise filtering by supplier type (factory/store), shipping time, dropshipping benefits, etc., helping you quickly locate target products.
It is particularly suitable for e-commerce operators, wholesalers, cross-border sellers, and dropshipping entrepreneurs who frequently conduct product research on 1688. Especially for users already utilizing AI coding assistants (like Claude, Cursor), they can perform data queries directly within the conversation without switching tools. For data analysts, the returned data can also be imported into analysis workflows to further explore market trends.
It is recommended to use this tool uniformly during batch product selection weeks or new product development cycles to improve research efficiency. Please note: weekly ranking data is only retained for the last 90 days, monthly rankings for the last 12 months—historical data cannot be retrieved. Additionally, query results are limited to a maximum of 100 items per page; for large datasets, please retrieve them in batches. Furthermore, this Skill relies on a network connection to the 1688 platform, so please ensure your environment has stable connectivity.
Key Features
Unlike the official 1688 ranking page or third-party data tools, this Skill supports direct querying via natural language interaction and allows combining over ten filtering conditions—such as price, sales volume, supplier type, and dropshipping services—in a single request, eliminating the need for manual webpage operations or writing complex SQL.
Limitations
Relies on network access to the 1688 platform. Weekly ranking data is only available for the last 90 days, monthly rankings for the last 12 months. A maximum of 100 results are returned per page, and there is no functionality to export historical data.
FAQ
How do I query 1688 weekly ranking data?
Set pageType=2 and fill the date parameter with the target week's Sunday date (e.g., 2025-06-15) to obtain the best-selling rankings for that week.
Does it support filtering by supplier type?
Yes, you can filter for stores (1) or factories (2) using the companyType parameter, or select seller levels like 'Chengxintong' via the shiLiType parameter.
Installation guide for AI assistants
If your AI coding assistant (Claude Code, Cursor, TRAE etc.) can see this page, send it this message to auto-install:
Visit https://321skill.com/skills/1688-hot-sales-ranking-3/raw/index.md to read the original Skill definition (Markdown format) for 1688-商品热销榜单, and install it according to the instructions.
Raw Markdown URL for AI: /skills/1688-hot-sales-ranking-3/raw/index.md