Scrapes second-hand item search results from Goofish (闲鱼/xianyu, goofish.com) — China's largest second-hand marketplace. Input: keyword, optional sort/filter params. Output: list of items with id, title, price, image, location, want-count per page (30 items/page). Use when user mentions goofish, 闲鱼, xianyu, 二手交易, second-hand marketplace China, 二手商品搜索, search used goods, scrape goofish listings, xianyu search results, collect second-hand prices, monitor used item prices, 闲鱼关键词搜索, 闲鱼数据采集, 批量抓取闲鱼, goofish scraper, goofish data, xianyu data extraction, 二手商品价格监控, used iPhone prices, 二手手机价格. Also applies to: price research on Chinese second-hand market, competitor product monitoring via used goods listings, inventory analysis.
Copy the skill from the publisher's repository (solutions/ecommerce/goofish-search-list/SKILL.md). PlugMCP lists it. We do not host or rewrite the file.
README
name: goofish-search-list
description: "Scrapes second-hand item search results from Goofish (闲鱼/xianyu, goofish.com) — China's largest second-hand marketplace. Input: keyword, optional sort/filter params. Output: list of items with id, title, price, image, location, want-count per page (30 items/page). Use when user mentions goofish, 闲鱼, xianyu, 二手交易, second-hand marketplace China, 二手商品搜索, search used goods, scrape goofish listings, xianyu search results, collect second-hand prices, monitor used item prices, 闲鱼关键词搜索, 闲鱼数据采集, 批量抓取闲鱼, goofish scraper, goofish data, xianyu data extraction, 二手商品价格监控, used iPhone prices, 二手手机价格. Also applies to: price research on Chinese second-hand market, competitor product monitoring via used goods listings, inventory analysis."
Goofish (闲鱼) — Search Results List
keyword + optional filters → list of 30 second-hand item cards per page (id, title, price, image, location, want-count)
Language
All process output to user (progress updates, process notifications) follows the user's language.
Objective
Extract second-hand item listing cards from Goofish keyword search results, supporting sort options, price range filters, and publish-date filters, with page-by-page pagination.
Prerequisites
Browser with an active Goofish session (logged-in account recommended for full results)
Target page is already open or will be opened: https://www.goofish.com/search?q={keyword}
Pre-execution Checks
1. Tool Readiness
If browser-act has been confirmed available in the current session → skip this step.
Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.
2. Login Verification
If login status for Goofish has been confirmed in the current session → skip this step.
Otherwise: open https://www.goofish.com/ and observe the page:
User avatar or account entry exists → logged in, continue
Login/register prompt → not logged in; inform user that login may be required for full results; assist login if needed
Capability Components
This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the user on the page. JS code is encapsulated in Python files under the directory, invoked via . is bash syntax; it is recommended to use the bash tool for execution.
scripts/
eval "$(python scripts/xxx.py {params})"
$(...)
Network Capture: trigger search and load results
Search requests use a dynamic sign token computed client-side — they cannot be reconstructed directly. Navigate to the search URL to trigger the API automatically.
Error handling: If the page shows a CAPTCHA slider ("Please slide to verify") instead of search results, the session has been rate-limited. Wait 5–10 minutes before retrying, or switch to a fresh browser session.
DOM: search result item cards (data extraction)
After navigating and waiting stable, extract all 30 item cards on the current page:
eval "$(python scripts/extract-search-items.py)"
Output example:
{
"items": [
{
"item_id": "1054668718340", // unique item ID
"category_id": "126862528", // category ID
"item_url": "https://www.goofish.com/item?id=1054668718340&categoryId=126862528",
"title": "美版iPhone 14 国行256G 纯原 原版原漆", // full title text
"image_url": "https://img.alicdn.com/bao/uploaded/...", // thumbnail URL
"price": "1810", // numeric string, CNY, no ¥ sign
"service_tag": "Apple/苹果256GB无任何维修", // condition/attribute tag or recency label, null if absent
"price_desc": "2人想要", // want-count or price-drop info, null if absent
"location": "广东" // seller's location province/city
}
],
"count": 30
}
DOM: apply sort and filter options (operation)
Apply sort order, publish-date filter, or price range before extracting. Call before running extract-search-items.py. After calling, wait stable before extracting.
DOM Pagination: Click the target page number button using goto-page.py {page}, then wait stable, then re-run extract-search-items.py. Page numbers appear in the pagination bar at the bottom of the search results.
Termination: When goto-page.py returns error: Page N not found — no more pages available, or the target page exceeds the pagination range displayed (typically up to 25 pages / 750 items).
Success Criteria
result count >= 1 and item_id non-null rate = 100% and price non-null rate >= 80%
Known Limitations
30 items per page (fixed by the site)
Maximum ~750 items accessible via pagination (25 pages × 30)
Seller username and user ID are not available in search cards — only seller location
Session rate limiting: accessing item detail pages rapidly after heavy search usage may trigger a CAPTCHA slider; mitigate by adding 1–2 second delays between page navigations
The sign token in search API requests is computed client-side; direct API replay without browser context is not supported — always trigger via page navigation
Execution Efficiency
Batch orchestration: Write a bash script to loop through keywords serially within a single session; do not parallelize within one browser (prone to triggering anti-scraping). Add 1–2 second delays between page navigations. To increase throughput, open multiple stealth browser sessions and distribute keywords across them
Test before batch execution: After writing a batch script, first test with 1–2 keywords/pages to verify the script runs correctly; only then run the full batch
Reduce redundant pre-operations: Navigate once per keyword, apply all filters at once before extracting, rather than navigating multiple times
Error resumption: Save results keyword-by-keyword and page-by-page during batch processing; on failure, resume from the last saved position
Before execution: If the file exists, read it first — it records unexpected situations encountered during past executions (e.g., a strategy has become ineffective); adjust strategy order accordingly.
After execution: If an unexpected situation is encountered (strategy became ineffective, page redesigned, anti-scraping upgraded, better path discovered), append a line:
{YYYY-MM-DD}: {what happened} → {conclusion}
Normal execution does not write to the file. Do not record what keywords were used or how many results were returned — those are task outputs, not experience.