/Catalogue/Prompt/browser-act/browser-act-skills-amazon-product-detail

Origin: github

amazon-product-detail

Amazon 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 c

by browser-act · updated 1mo ago · imported from GitHub

Installs0+0/7d
Security score92/100
Retention 14d0%
GitHub stars6K

Skill logic

Execution graph
User message
Prompt rewrites behaviour
Response

SKILL.md

View on GitHub ↗

Amazon — Product Detail

Input any Amazon product URL → output full product record (100+ fields).

Language

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

Objective

Extract the complete product record from any Amazon detail page URL across all Amazon regional TLDs, including price, ratings breakdown, attributes, variants, bestseller ranks, seller info, delivery, and sample reviews.

Prerequisites

  • Target page is already open in the browser: any Amazon product URL (e.g. https://www.amazon.com/dp/{ASIN}, https://www.amazon.com/gp/product/{ASIN}, https://www.amazon.co.uk/dp/{ASIN}, or the canonical SEO-slug URL https://www.amazon.com/{slug}/dp/{ASIN})
  • No login required

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. Its role is equivalent to copy-pasting on the user's behalf — the data is already on screen, automation merely saves time. JS code is encapsulated in Python files under the scripts/ directory, invoked via browser-act --session {name} eval "$(python scripts/xxx.py {params})". The $(...) is bash command substitution — it runs the python script, captures its printed JS text, and hands that JS string as a single argument to browser-act eval. Do not run eval "$(python ...)" as a bare shell command; that would ask bash to execute the JS as shell, which fails.

DOM: extract full product detail from current product page

Amazon detail pages are server-rendered HTML — no XHR/fetch API for detail data. All fields come from stable DOM selectors:

  1. navigate {any Amazon product URL, e.g. https://www.amazon.com/dp/{ASIN}}
  2. wait stable
  3. Extract: browser-act --session {name} eval "$(python scripts/extract-product-detail.py)"

On error paths, the script returns:

  • {"error": true, "message": "product_not_found: 404 page"} when the URL resolves to Amazon's not-found page
  • {"error": true, "message": "productTitle not found - page may not be a product detail page"} when the URL resolves to a non-detail page (e.g. search results, homepage)

Output example:

{
  "asin": "B09S3HNMHF",                                // parsed from /dp/{ASIN} or /gp/product/{ASIN} path segment
  "url": "https://www.amazon.com/dp/B09S3HNMHF",       // canonical origin + pathname
  "title": "Samsung 14\" Galaxy Chromebook Go ...",    // #productTitle
  "brand": "Samsung",                                  // #bylineInfo, falls back to attributesMapped.Brand/Manufacturer
  "price": {"value": 179.99, "currencyRaw": "$", "raw": "$179.99"},   // current buybox price, null when no offer
  "listPrice": {"value": 190.99, "currencyRaw": "$", "raw": "$190.99"}, // strike-through list price, null when absent
  "stars": 4.3,                                        // 0-5 average rating, null when no reviews
  "reviewsCount": 632,                                 // total review count, null when no reviews
  "starsBreakdown": {"5 star": 70, "4 star": 13, "3 star": 5, "2 star": 3, "1 star": 9},  // percentage per star bucket
  "answeredQuestions": null,                           // number, null when absent or non-numeric
  "inStock": true,                                     // derived from availability text, null when unclear
  "inStockText": "In Stock",                           // raw #availability text
  "delivery": "FREE delivery Wednesday, July 15",      // primary delivery message, null when absent
  "fastestDelivery": null,                             // secondary/fastest delivery, null when absent
  "returnPolicy": "30-day return period",              // null when absent
  "breadCrumbs": "Electronics > Computers > Laptops > Traditional Laptops",  // joined with ' > ', "" when absent
  "features": ["Slim design", "12-hour battery", "..."],  // #feature-bullets bullet points
  "description": "A+ in performance and value...",     // #productDescription, null when absent
  "bookDescription": null,                             // #bookDescription_feature_div for books, null otherwise
  "thumbnailImage": "https://m.media-amazon.com/images/I/51...jpg",
  "highResolutionImages": ["https://m.media-amazon.com/images/I/51...SY450_.jpg", "..."],  // from #imgTagWrapperId data-a-dynamic-image JSON
  "galleryThumbnails": ["https://m.media-amazon.com/images/I/41...jpg", "..."],
  "productOverview": [{"key": "Brand", "value": "Samsung"}, {"key": "Model Name", "value": "XE340XDA-KA2US"}],  // #productOverview_feature_div table
  "attributes": [{"key": "Color", "value": "Silver"}, {"key": "Item Weight", "value": "3.2 pounds"}],  // technical spec tables
  "attributesMapped": {"Brand": "Samsung", "Item Weight": "3.2 pounds"},  // flat merged key-value
  "bestsellerRanks": [
    {"rank": 44, "category": "Computers & Accessories", "url": "https://www.amazon.com/gp/bestsellers/pc/..."},
    {"rank": 5, "category": "Traditional Laptop Computers", "url": "https://www.amazon.com/gp/bestsellers/pc/13896615011/..."}
  ],
  "variantAttributes": [{"key": "Color", "value": "Silver"}],  // currently selected variant dimensions
  "variantAsins": ["B09S3HNMHF", "B0G4WBD45V", "B0GG2CPM1K"],  // all variant ASINs from swatches, empty [] when no variants
  "seller": {"name": "Amazon.com", "id": "ATVPDKIKX0DER", "url": "https://www.amazon.com/sp?seller=..."},  // null when no seller link
  "isAmazonChoice": false,                             // .ac-badge-rectangle presence
  "amazonChoiceText": null,                            // Amazon's Choice label text, null when badge absent
  "monthlyPurchaseVolume": "6K+ bought in past month", // #social-proofing-faceout-title-tk_bought, null when absent
  "hasAPlusContent": true,                             // #aplus presence
  "hasBrandStory": false,                              // #brand-snapshot_feature_div presence
  "aiReviewsSummary": null,                            // AI-generated review summary text, null when absent
  "reviewsLink": "https://www.amazon.com/product-reviews/B09S3HNMHF",
  "productPageReviews": [
    {"username": "Jane D.", "ratingScore": 5, "reviewTitle": "Great product!", "reviewDescription": "Really happy with this purchase.", "date": "Reviewed in the United States on January 15, 2025"}
  ],
  "videosCount": null,                                 // count of gallery video slots, null when none
  "locationText": "Update location",                   // #glow-ingress-line2 delivery-location display
  "loadedCountryCode": "com"                           // amazon.{tld} suffix used to detect region (com, co.uk, de, co.jp, etc.)
}

Success Criteria

response.error is not present AND response.asin matches /^[A-Z0-9]{10}$/ AND response.title is a non-empty string

Known Limitations

  • answeredQuestions is null on many pages because Amazon's Q&A widget layout varies; when present, the field surfaces the numeric count.
  • aiReviewsSummary is populated only on products with sufficient reviews and Amazon's AI summary feature enabled.
  • variantAsins covers ASINs visible in swatch elements (data-defaultasin, data-asin, data-dp-url); large twister structures whose variants are lazy-loaded via XHR only surface the visible subset on first render.
  • productPageReviews returns the sample reviews Amazon renders on the detail page (typically 6-10 items) — for full review pagination use a separate review-scraping capability.
  • Prices, delivery, and stock reflect the browsing session's inferred country/zip; use a proxy in the target region to fetch localized values.
  • CAPTCHA / anti-bot interstitials will cause the script to return productTitle not found — surface this to the caller so it can retry with a fresh session or proxy.
  • Bestseller ranks are only populated when the product page renders the "Best Sellers Rank" row in detail bullets or product-details tables.
  • Amazon A/B experiments occasionally reorder DOM containers; on failure to extract a specific field, inspect page structure and open an issue rather than assuming Amazon-wide breakage.

Execution Efficiency

  • Batch orchestration: Write a bash script iterating ASINs serially inside one browser session; do not parallelize inside one browser. Insert a 3-6 second delay between ASINs to reduce anti-scraping pressure. For higher throughput, open multiple stealth sessions (different fingerprints/proxies) and shard the ASIN list.
  • Test before batch execution: After writing the batch script, first test with 2-3 ASINs before running the full enrichment. Never skip testing.
  • Reduce redundant pre-operations: Reuse the same browser session across ASINs — only navigate + wait stable + eval per item, no need to re-open.
  • Error resumption: Persist per-ASIN JSON as it completes so a partial crash resumes from the failed ASIN.

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/amazon-scraper-amazon-product-detail.memory.md (working directory is determined by the Agent running the Skill, typically the project root or current working directory)

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.

Discussion

No comments yet — start the thread.

Sign in to join the discussion.

/More from browser-act/skills

browser-act· 1mo agoCommunity
goofish-search-list

Prompts · Python · v0.1.0

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.

#agent-infrastructure#ai-agents#automation

0 6K
browser-act· 1mo agoCommunity
taobao-keyword-search

Prompts · Python · v0.1.0

Search Taobao and Tmall product listings by keyword, returning paginated product cards with title, price, shop, image, sales, and tags. Use when user asks to search Taobao, find products on Taobao/Tmall, scrape Taobao search results, get product listings from Taobao, collect Taobao items by keyword, 搜索淘宝, 淘宝关键词搜索, 采集淘宝商品, 抓取淘宝搜索结果, 淘宝天猫商品列表. Also applies to bulk keyword searches, price monitoring across keywords, and competitive product research on Taobao.

#agent-infrastructure#ai-agents#automation

0 6K
browser-act· 1mo agoCommunity
taobao-product-detail

Prompts · Python · v0.1.0

Fetch full product detail from a Taobao or Tmall product page by itemId, returning title, price, shop info, images, SKU variants, and product attributes. Use when user asks to get product details from Taobao, scrape a Taobao item page, extract product info by item ID, fetch Tmall product data, 抓取淘宝商品详情, 获取淘宝商品信息, 淘宝商品页面采集, 天猫商品详情, 按商品ID获取信息. Also applies to building product databases, price tracking by itemId, and product comparison research.

#agent-infrastructure#ai-agents#automation

0 6K
browser-act· 1mo agoCommunity
taobao-product-reviews

Prompts · Python · v0.1.0

Fetch customer reviews for a Taobao or Tmall product by itemId, returning reviewer name, date, purchased variant, review text, and photo URLs. Use when user asks to get product reviews from Taobao, scrape Taobao customer feedback, extract buyer reviews by item ID, collect Tmall ratings and comments, 采集淘宝商品评价, 抓取淘宝买家评论, 获取淘宝商品评论, 天猫商品评价抓取, 按商品ID获取评价. Also applies to sentiment analysis of product reviews, building review datasets, and monitoring product rating changes.

#agent-infrastructure#ai-agents#automation

0 6K