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goofish-search-list

Extract Goofish keyword search results into structured JSON via DOM parsing.

5.3k|252|Updated Feb 6, 2026
Search Tags:#scraping#ecommerce#data-extraction#web-automation#xianyu#goofish
One-click install
npx skills add https://github.com/browser-act/skills --skill goofish-search-list
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: goofish-search-list
Source: https://github.com/browser-act/skills/tree/main/solutions/ecommerce/goofish-search-list
Command: npx skills add https://github.com/browser-act/skills --skill goofish-search-list

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Goofish search result scraping eliminates the manual, tedious task of collecting item cards from a large Chinese second-hand marketplace, providing structured data ready for analysis.

Core Features & Use Cases

  • Keyword-based extraction of 30-item result cards per page (id, title, price, image, location, want-count)
  • Supports sort options, publish-date filters, and price-range filters with page-by-page pagination
  • Use Case: monitor used item prices, compare listings, and build a price-history dataset for goofish/xianyu market analysis.

Quick Start

Enter a keyword and optional filters to fetch the first page of 30 Goofish items.

Frequently Asked Questions about goofish-search-list

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract Goofish search results into structured data?▼

Goofish search results extraction normalizes keyword search item cards into structured JSON data. It captures 30 items per page, including id, title, price, image, location, and want-count, using browser-based DOM extraction through Python scripts.

Can I filter Xianyu item listings by publish date and price range when scraping?▼

Xianyu item scraping supports sort options, publish-date filters, and price-range filters. You can apply these parameters alongside keyword queries to narrow down the extracted item cards before pagination.

What is the best way to scrape second-hand marketplace data for price history analysis?▼

Scraping second-hand marketplace data for price history analysis involves paginating through Goofish search results. By extracting 30 normalized item cards per page, you can build datasets to monitor used item prices and compare listings over time.

Does Goofish data extraction work with browser automation for paginated results?▼

Goofish data extraction uses browser-based DOM extraction through Python scripts to read page content. It supports page-by-page pagination to systematically collect item listings and emit structured JSON output.

What limitations should I expect when scraping Goofish item cards?▼

Goofish scraping extracts 30 items per page and relies on browser-based DOM extraction. You must handle pagination sequentially to gather more results, and structured output is limited to visible item card fields like id, title, price, and location.

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