Mmcp.market

1688-product-detail skill

by browser-act·browser-act/skills·6.0k stars·MIT

Extracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688, 1688.com, wholesale China, alibaba wholesale, B2B China sourcing, Chinese wholesale scraper, 1688 product scrape, 1688 offer, 1688 detail, extract 1688 data, pull 1688 listings, get wholesale price, 1688 supplier info, factory stats 1688, 1688 SKU variants, 1688 product attributes, 1688 shop score, DSR score 1688, 1688 buyer protection, 1688 cross-border, 1688 dropship. Also applies to: scraping bulk product data from 1688 by offer ID list, monitoring 1688 supplier metrics, extracting 1688 pricing tiers for resale analysis.

A100/100content scan

Is the 1688-product-detail skill safe?

Clean: nothing in its files matched our rules. We read 5 files in the folder on 2026-09-28.

No findings.

Install the 1688-product-detail skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/browser-act/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -r /tmp/skills/solutions/ecommerce/1688-product-detail ~/.claude/skills/1688-product-detail
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

1688.com — Product Detail Extraction

Navigate to a 1688 product page → extract 50+ fields including pricing tiers, SKU variants, seller stats, attributes, promotions

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Extract complete wholesale product data from a 1688.com offer detail page using embedded page data and network capture for supplier metrics.

Prerequisites

  • Target product detail page is open in the browser: https://detail.1688.com/offer/{offer_id}.html
  • No login required for product detail pages (data is publicly accessible)

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.

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, never bypassing authentication or access controls. JS code is encapsulated in Python files under the scripts/ directory, invoked via eval "$(python scripts/xxx.py {params})". $(...) is bash syntax; it is recommended to use the bash tool for execution.

DOM: Extract core product data (title, pricing, images, seller, flags)

After navigating to the product page and waiting for page load:

eval "$(python scripts/extract-product-detail.py '{offer_id}')"

Parameters:

  • offer_id: Numeric 1688 offer/product ID (e.g., 927875250705)

Output example:

{
  "offerId": "927875250705",
  "title": "新款苹果18promax手机壳磁吸...",
  "unit": "个",
  "category": { "topCategoryId": 7, "postCategoryId": 132918005 },
  "pricing": {
    "tiers": [
      { "minQty": "30", "price": "7.99" },
      { "minQty": "100", "price": "7.79" }
    ],
    "priceDisplayType": "range",
    "minOrderQty": 30,
    "currency": "CNY"
  },
  "sales": {
    "totalSold": 308417,
    "displaySaleNum": "10万+",
    "saleCountLabel": "全网销量"
  },
  "images": ["https://cbu01.alicdn.com/img/ibank/...jpg"],
  "attributes": {
    "材质": "优质TPU",
    "款式": "后盖款",
    "功能": "防震,磁吸,防磨,防摔",
    "适用型号": "iPhone17,iphone17pro..."
  },
  "skuCount": 339,
  "skuWeightData": [
    { "weight": 40, "length": 17, "width": 7, "height": 1, "volume": 119 }
  ],
  "seller": {
    "companyName": "佛山市南海区三丰手机配件有限公司",
    "loginId": "fssf06",
    "memberId": "b2b-2850655109d72ea",
    "userId": 2850655109,
    "shopUrl": "https://shop1460393846166.1688.com",
    "cardType": "cjgc",
    "isPmPlus": true,
    "serviceScore": "4.5分",
    "buyerRepeatRate": "65.82%"
  },
  "offerFlags": {
    "isSkuOffer": true,
    "isPreSell": false,
    "isConsignMarketOffer": true,
    "isDistribution": true,
    "isC

DOM: Extract SKU variants (color/model combinations with weight/dimensions)

eval "$(python scripts/extract-sku-details.py '{offer_id}')"

Parameters:

  • offer_id: Numeric 1688 offer/product ID

Output example:

{
  "offerId": "927875250705",
  "skuCount": 339,
  "skuRangePrices": [
    { "price": "7.99", "beginAmount": "30" },
    { "price": "7.79", "beginAmount": "100" }
  ],
  "skus": [
    {
      "skuId": 5833485852524,
      "specId": "...",
      "attrs": { "颜色": "黑色", "适用型号": "iPhone17" },
      "saleCount": 0,
      "canBookCount": 9999,
      "isPromotionSku": false,
      "packInfo": { "weight": 40, "length": 17, "width": 7, "height": 1, "volume": 119 }
    }
  ],
  "skuImageMap": {}
}

DOM: Extract coupon and promotion data

eval "$(python scripts/extract-promotions.py '{offer_id}')"

Parameters:

  • offer_id: Numeric 1688 offer/product ID

Output example:

{
  "offerId": "927875250705",
  "coupons": [
    { "couponType": "INTERACT", "couponContent": "满100减5券" }
  ],
  "promotionModel": {
    "buttonName": "领券",
    "promotionList": [
      {
        "type": "INTERACT",
        "name": "互动优惠券",
        "summary": "入会有礼券",
        "promotionItems": [
          {
            "label": "满100减5券",
            "availablePeriod": "有效期：2026.05.28 00:00:00-2026.11.24 23:59:59",
            "canApply": true
          }
        ]
      }
    ]
  },
  "activity": {
    "activityType": null,
    "activityName": null,
    "activityUrl": null,
    "countdown": null,
    "activityId": null
  },
  "bannerImage": ""
}

DOM: Extract seller params (for shopcard network capture)

eval "$(python scripts/extract-seller-params.py '{offer_id}')"

Parameters:

  • offer_id: Numeric 1688 offer/product ID

Output example:

{
  "offerId": "927875250705",
  "seller": {
    "companyName": "佛山市南海区三丰手机配件有限公司",
    "loginId": "fssf06",
    "memberId": "b2b-2850655109d72ea",
    "userId": 2850655109,
    "shopUrl": "https://shop1460393846166.1688.com",
    "cardType": "cjgc",
    "serviceScore": "4.5分",
    "buyerRepeatRate3m": "65.82%"
  },
  "shopcardParams": {
    "offerId": "927875250705",
    "userId": 0,
    "offerMemberTags": [4336705, 519170, "..."],
    "sellerUserId": 2850655109,
    "sellerMemberId": "b2b-2850655109d72ea",
    "topCategoryId": 7,
    "offerModelSign": { "isBuyerProtection": true, "isDistribution": true },
    "sellerIdentity": "cjgc",
    "sellerWinportUrlMap": { "indexUrl": "...", "defaultUrl": "..." },
    "winportUrl": "https://shop1460393846166.1688.com"
  }
}

Network Capture: Get shop scores and metrics (shopcard API)

The shopcard API uses dynamic sign tokens — let the page JS handle it, read from network traffic.

After the product detail page loads fully (wait stable), the shopcard request fires automatically:

  1. wait stable
  2. network requests --type xhr,fetch --filter h5api.m.1688.com
  3. Find request with URL containing mtop.1688.moga.pc.shopcard
  4. network request

Endpoint characteristic: URL contains mtop.1688.moga.pc.shopcard

If the shopcard request is not in traffic (navigated away or cleared), reload the product page:

  1. navigate https://detail.1688.com/offer/{offer_id}.html
  2. wait stable
  3. Repeat steps 2–4 above

Error handling: If request not found after page reload, check if the product page loaded correctly (screenshot), then retry once. If still unavailable, shopcard data is unavailable for this offer.

Output example:

{
  "api": "mtop.1688.moga.pc.shopcard",
  "data": {
    "model": {
      "shopName": "佛山市南海区三丰手机配件有限公司",
      "shopType": "cjgc",
      "iconType": "cjgc",
      "mainCategoryName": "手机配件",
      "shopUrl": "https://shop1460393846166.1688.com",
      "tpYear": 11,
      "shopData": [
        { "dataKey": "店铺回头率", "dataValue": "66%" },
        { "dataKey": "店铺服务分", "dataValue": "4.5", "unit": "分" },
        { "dataKey": "准时发货率", "dataValue": "- %" },
        { "dataKey": "店铺好评率", "dataValue": "99.9%" }
      ],
      "shopButton": {
        "fuzzyFavCount": "8.6k粉丝",
        "attentionRelation": false
      }
    }
  }
}

Network Capture: Get DSR review summary (queryDsrRateDataV2 API)

After page load, the DSR scores request fires automatically alongside shopcard:

  1. wait stable
  2. network requests --type xhr,fetch --filter h5api.m.1688.com
  3. Find request with URL containing querydsrratedatav2
  4. network request

Endpoint characteristic: URL contains mtoprateservice.querydsrratedatav2

Error handling: Same as shopcard — if not found, navigate to the product page and retry. The DSR API fires with the POST param loginId = seller loginId and offerId; both come from extract-seller-params.py output.

Output example:

{
  "data": {
    "model": {
      "goodRates": 99.9,
      "goodsGrade": 5.0,
      "fulfillmentDataList": [
        { "name": "商品好评", "value": "100%" },
        { "name": "按时发货" },
        { "name": "商品退款" }
      ],
      "commonTagNodeList": [
        { "name": "全部", "count": 2497 },
        { "name": "有图", "count": 6 },
        { "name": "好评", "count": 2494 }
      ],
      "impressionTagNodeList": [
        { "name": "价格很便宜", "count": 6 },
        { "name": "质量很好", "count": 5 }
      ]
    }
  }
}

Composite: Full product data extraction

Combines DOM extraction with network capture for complete data. For each offer ID:

  1. navigate https://detail.1688.com/offer/{offer_id}.html
  2. wait stable
  3. eval "$(python scripts/extract-product-detail.py '{offer_id}')" → core data
  4. eval "$(python scripts/extract-sku-details.py '{offer_id}')" → SKU variants
  5. eval "$(python scripts/extract-promotions.py '{offer_id}')" → coupons/activity
  6. network requests --type xhr,fetch --filter h5api.m.1688.com → locate shopcard and DSR requests
  7. network request → shop scores
  8. network request → review stats
  9. Merge all results by offerId

Enum Parameters

More skills from browser-act/skills

  • Aairbnb-listing-detailFetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.
  • Aairbnb-search-listingExtracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.
  • Aamazon-alexa-qaAmazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.
  • Aamazon-asin-lookup-api-skillThis skill helps users extract structured product details from Amazon using a specific ASIN (Amazon Standard Identification Number). Use this skill when the user asks to get Amazon product details by ASIN, lookup Amazon product title and price using ASIN, extract Amazon product ratings and reviews count for a specific ASIN, check Amazon product availability and current price, get Amazon product description and features via ASIN, enrich product catalog with Amazon data using ASIN, monitor Amazon product price changes for specific ASINs, retrieve Amazon product brand and material information, fetch Amazon product images and specifications by ASIN, validate Amazon ASIN and get product metadata.
  • Aamazon-best-selling-products-finder-api-skillThis skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market analysis.
  • Aamazon-bestseller-listingAmazon Best Sellers listing scraper: extract product cards from any Amazon Best Sellers (zgbs) or /gp/bestsellers/ category page — returns rank (position on chart), asin, title, url, image, imageAlt, price, stars, reviewCount, ratingRaw per item, plus category metadata (categoryName, categoryFullName, categoryUrl) and pagination state (currentPage, hasNextPage, nextPageUrl). Works across all Amazon regional TLDs (amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, etc.). Use when user mentions Amazon Best Sellers, Amazon bestsellers, Amazon top 100, Amazon zgbs, Amazon /zgbs/, Amazon /gp/bestsellers/, Amazon Best Sellers Rank, Amazon BSR, Amazon top ranked products, Amazon top-selling products, Amazon chart, Amazon category ranking, Amazon best sellers by category, Amazon best sellers electronics, Amazon best sellers kitchen, Amazon best sellers toys, scrape Amazon bestsellers, extract Amazon top 100, Amazon rank scraper, Amazon best seller list, Amazon leaderboard, Amazon trending products, discover trending Amazon products, Amazon niche discovery, Amazon top ranked ASINs. Also applies to competitive intelligence via ranking snapshots, spotting up-and-coming products, sourcing bestseller ASINs for further enrichment, tracking rank changes over time, and building bestseller-per-category datasets.
  • Aamazon-buy-box-monitor-api-skillThis skill helps users extract basic product details other sellers prices and seller ratings from Amazon via ASIN automatically using the BrowserAct API. Agent should proactively apply this skill when users express needs like query Amazon buy box information, monitor Amazon product prices, extract Amazon product details by ASIN, check other sellers prices on Amazon, get Amazon seller ratings and feedback count, monitor buy box ownership for a specific ASIN, track Amazon fulfillment methods for competitors, compare Amazon product prices across different sellers, retrieve Amazon buy box availability status, analyze Amazon seller profile details.
  • Aamazon-competitor-analyzerScrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
  • Aamazon-listing-competitor-analysis-skillThis skill helps users analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked product does right in content keywords or visuals, find market gaps and unmet buyer needs, turn competitor research into opportunity maps for their brand, identify keyword placement patterns on rival listings, extract SEO insights from Amazon product pages, reverse-engineer competitor bullet and title strategies, mine competitor reviews for buyer psychology, compare seller and A plus content patterns, run gap analysis before launching a new SKU, research why a listing wins conversion signals, synthesize whitespace you can own versus the diagnosed listing, or say just look at this ASIN with a competitive or optimization angle.
  • Aamazon-product-api-skillThis skill helps users extract structured product listings from Amazon, including titles, ASINs, prices, ratings, and specifications. Use this skill when users want to search for products on Amazon, find the best selling brand products, track price changes for items, get a list of categories with high ratings, compare different brand products on Amazon, extract Amazon product data for market research, look for products in a specific language or marketplace, analyze competitor pricing for keywords, find featured products for search terms, get technical specifications like material or color for product lists.
  • Aamazon-product-detailAmazon product detail page scraper: extract full product data from any open Amazon product detail URL (any /dp/{asin} or /gp/product/{asin} page across all Amazon regional TLDs) — returns asin, url, title, brand, price, listPrice, stars, reviewsCount, starsBreakdown (5/4/3/2/1 star percentages), answeredQuestions, inStock, inStockText, delivery, fastestDelivery, returnPolicy, breadCrumbs, features (bullet points), description, bookDescription, thumbnailImage, highResolutionImages, galleryThumbnails, productOverview (Brand/Model/etc.), attributes (tech spec table), attributesMapped (flat key-value), bestsellerRanks (rank + category + url), variantAttributes (currently selected color/size/style), variantAsins, seller (name + id + url), isAmazonChoice, amazonChoiceText, monthlyPurchaseVolume, hasAPlusContent, hasBrandStory, aiReviewsSummary, reviewsLink, productPageReviews (sample), videosCount, locationText, loadedCountryCode. Works on amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, amazon.com.mx, amazon.com.br, amazon.nl, amazon.se, amazon.sg, amazon.ae, amazon.sa, amazon.pl, amazon.tr, amazon.eg. Use when user mentions Amazon product page, Amazon /dp/, Amazon dp URL, Amazon ASIN scraper, Amazon product detail, Amazon PDP, Amazon product data, Amazon product info, Amazon product fields, Amazon product attributes, Amazon full field extraction, Amazon per-ASIN enrichment, Amazon rating breakdown, Amazon stars breakdown, Amazon bestseller rank, Amazon BSR, Amazon variants, Amazon variant ASINs, Amazon color size options, Amazon feature bullets, Amazon A+ content, Amazon brand story, Amazon AI review summary, Amazon bought in past month, Amazon monthly sales volume, Amazon Amazon's Choice badge, Amazon seller info, scrape Amazon product, enrich Amazon ASIN, Amazon ASIN details, Amazon product review data. Also applies to bulk ASIN enrichment from a list of URLs, competitive product research, brand catalog audits, price and stock monitoring per ASIN, and building a normalized product dataset from a list of Amazon URLs.
  • Aamazon-product-search-api-skillThis skill is designed to help users automatically extract product data from Amazon search results. The Agent should proactively apply this skill when users request searching for products related to keywords, finding best-selling items from specific brands, monitoring product prices and availability on Amazon, extracting product listings for market research, collecting product ratings and review counts for competitive analysis, finding specific products with a maximum count, searching Amazon in different languages for localized results, tracking monthly sales estimates for brand products, gathering product URLs and titles for a product catalog, scanning Amazon for Best Seller tags in a specific category, monitoring shipping and delivery information for brand items, building a structured dataset of Amazon search results.

All agent skills → · MCP servers