ecommerce-product-detail
This Claude Code skill extracts complete product information from e-commerce product pages across major platforms including Amazon, Shopify, eBay, Walmart, and Etsy. Use it to retrieve product details such as name, price, brand, images, identifiers (ASIN, SKU, EAN, UPC), stock availability, ratings, variants, and seller information from any public product URL, keyword search, or product identifier lookup.
git clone --depth 1 https://github.com/browser-act/skills /tmp/ecommerce-product-detail && cp -r /tmp/ecommerce-product-detail/solutions/ecommerce/ecommerce-product-detail ~/.claude/skills/ecommerce-product-detailSKILL.md
# E-commerce — Product Detail
> Product URL / keyword / SKU → complete product data (name, price, brand, images, identifiers, availability, rating, variants)
## Language
All process output to user (progress updates, process notifications) follows the user's language.
## Objective
Extract complete product information from any publicly accessible e-commerce product page using a universal multi-layer extraction strategy (JSON-LD → platform-specific DOM → OG meta → microdata).
## Prerequisites
- Target browser is open and connected
- No login required for public product pages
## 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 "$(python scripts/xxx.py {params})"`. Use the bash tool for execution.
### DOM: Extract product data from current product page
Navigate to the product URL first, then extract:
```bash
eval "$(python scripts/extract-product.py)"
```
Output example:
```json
{
"url": "https://www.amazon.com/dp/B09WNK39JN",
"name": "Amazon Echo Pop",
"price": 39.99,
"price_currency": "USD",
"brand": "Amazon",
"image": "https://m.media-amazon.com/images/I/61bTwy0ooPL.jpg",
"images": ["https://...jpg", "https://...jpg"],
"description": "Compact smart speaker with Alexa...",
"category": ["Electronics", "Smart Speakers"],
"sku": "B09WNK39JN",
"gtin": null,
"mpn": null,
"availability": "InStock",
"rating": 4.7,
"review_count": 103789,
"variants": [{"name": "Charcoal", "sku": "B09WNK39JN", "price": 39.99}],
"seller": "Amazon",
"identifiers": {"ASIN": "B09WNK39JN", "Best Sellers Rank": "#1 in Smart Speakers"},
"_platform": "amazon",
"_source": "json-ld"
}
```
### Composite: Keyword or SKU → product detail
When input is a keyword, ASIN/SKU, or EAN/UPC rather than a direct product URL:
**Step 1 — Navigate to search URL based on input type:**
| Input type | Target site | URL pattern |
|-----------|-------------|-------------|
| ASIN (10-char alphanumeric) | Amazon | `https://www.amazon.com/dp/{ASIN}` |
| Keyword | Amazon | `https://www.amazon.com/s?k={keyword_urlencoded}` |
| Keyword | eBay | `https://www.ebay.com/sch/i.html?_nkw={keyword_urlencoded}` |
| Keyword | Walmart | `https://www.walmart.com/search?q={keyword_urlencoded}` |
| Keyword + `--site` specified | Any site | `https://{site}/search?q={keyword_urlencoded}` |
| Keyword (no site) | Cross-site | `https://www.google.com/search?tbm=shop&q={keyword_urlencoded}` |
| EAN / UPC / GTIN | Cross-site | `https://www.google.com/search?tbm=shop&q={identifier}` |
**Step 2 — If landed on a search/listing page (multiple results):**
1. `wait stable`
2. `eval "$(python scripts/extract-listing.py --max-results 3)"` — get top 3 results
3. Pick the most relevant product URL from `items[0].url`
4. `navigate {product_url}` → `wait stable`
**Step 3 — Extract product data:**
```bash
eval "$(python scripts/extract-product.py)"
```
Note: `scripts/extract-listing.py` is located in `../ecommerce-listing/scripts/extract-listing.py` if used as a standalone Skill install; otherwise reference the listing Skill.
## Success Criteria
`result.name != null AND (result.price != null OR result.availability != null)`
## Known Limitations
- Amazon bot detection: direct navigation to a product URL may redirect to a CAPTCHA or bot-check page on fresh sessions. Navigate from `https://www.amazon.com` first to establish session cookies, then navigate to the product page
- eBay product pages may require navigating from `https://www.ebay.com` first; use `solve-captcha` if a challenge appears
- Some sites render product data entirely via client-side JavaScript; always use `wait stable` before extracting
- Price may be null for out-of-stock items or when login is required to view pricing
- Variant data completeness depends on whether the site includes full variant markup in JSON-LD
## Execution Efficiency
- **Batch orchestration**: Write a bash script to loop through product URLs serially within a single session; add 1–2 second intervals between requests to avoid triggering anti-scraping restrictions
- **Test before batch execution**: Test with 1–2 URLs before running the full batch
- **Error resumption**: Save results item by item; on failure, resume from the breakpoint rather than starting over
## Experience Notes
Path: `{working-directory}/browser-act-skill-forge-memories/ecommerce-scraper-ecommerce-product-detail.memory.md`
**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}`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.
Fetches 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.
Extracts 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.
Amazon 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.
This 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.
This 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.
Amazon 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.
This 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.