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

Origin: github

walmart-product-detail

Walmart product detail page extractor: given a walmart.com product URL (walmart.com/ip/...), extract full product data including itemId, title, brand, model, UPC, price, wasPrice, currency, availability, category path, seller info, all images, shortDescription, longDescription, product highlights, full specifications as key-value map, color/size variants with item IDs, fulfillment options (shipping/pickup/delivery with dates), return policy, and review summary with rating breakdown. Use when user mentions walmart product detail, walmart item page, walmart product page, scrape walmart product, extract walmart item, walmart product data, walmart item details, walmart product info, walmart product scraper, walmart item scraper, walmart product URL, walmart ip URL, walmart.com/ip, walmart specifications, walmart product specs, walmart product images, walmart variants, walmart color options, walmart size options, walmart seller info, walmart return policy, walmart fulfillment options, walmart shipping info, walmart availability, walmart product enrichment. Also applies to enriching a list of walmart product URLs with full details, monitoring walmart product price and availability changes, building a walmart product catalog, competitive product research on walmart, and batch collection of full product data from walmart item IDs.

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 ↗

Walmart — Product Detail

product URL → full structured product data from walmart.com product detail page

Language

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

Objective

Extract complete product data from a Walmart product detail page, including pricing, images, specifications, variants, fulfillment options, and review summary.

Prerequisites

  • Target product page is open in the browser: https://www.walmart.com/ip/{product-slug}/{item-id}

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 eval "$(python scripts/xxx.py {params})". $(...) is bash syntax; it is recommended to use the bash tool for execution.

Below are all atomic capabilities discovered and verified during the exploration phase, listed by command template with parameters. Simply invoke them as needed — no need to read scripts/*.py source code or re-verify. Only inspect scripts when execution fails for troubleshooting. Combine freely as needed during execution.

DOM: extract full product data from current product page

Navigate to the product URL first, then extract:

  1. navigate "https://www.walmart.com/ip/{product-slug}/{item-id}"
  2. wait stable
  3. eval "$(python scripts/extract-product-detail.py)"

The item-id is the numeric Walmart item ID (usItemId). The product-slug portion of the URL does not affect which product is loaded — only the item-id matters.

Output example:

{
  "itemId": "18656507313",
  "url": "https://www.walmart.com/ip/HP-14-N150-4-128-Blue/18656507313",
  "title": "HP 14 inch HD Windows Laptop Intel Processor N150 4GB 128GB UFS Waterfall Blue",
  "brand": "HP",
  "brandUrl": "https://www.walmart.com/search?q=HP&facet=brand:HP",
  "model": "14-ep2112wm",
  "upc": "199764359186",
  "manufacturerProductId": "CQ5J6UA#ABA",
  "classType": "VARIANT",
  "price": 229,
  "priceString": "$229.00",
  "currencyUnit": "USD",
  "wasPrice": null,
  "availability": "IN_STOCK",
  "category": [
    {"name": "Electronics", "url": "https://www.walmart.com/cp/electronics/3944"},
    {"name": "Laptops", "url": "https://www.walmart.com/cp/laptops/3951"}
  ],
  "sellerId": "F55CDC31AB754BB68FE0B39041159D63",
  "sellerName": "Walmart.com",
  "sellerDisplayName": "Walmart.com",
  "sellerType": "INTERNAL",
  "sellerAverageRating": null,
  "sellerReviewCount": null,
  "averageRating": 4.4,
  "numberOfReviews": 63,
  "thumbnail": "https://i5.walmartimages.com/seo/HP-14.jpeg",
  "images": ["https://i5.walmartimages.com/seo/image1.jpeg", "https://i5.walmartimages.com/asr/image2.jpeg"],
  "shortDescription": "The HP 14 inch Laptop PC has it all...",
  "longDescription": "<ul><li><strong>Intel N150 processor:</strong> ...</li></ul>",
  "productHighlights": [
    {"name": "RAM memory", "value": "4 GB"},
    {"name": "Processor", "value": "N150"}
  ],
  "specifications": {
    "RAM memory": "DDR5",
    "OS": "Windows 11",
    "Screen size": "14 in",
    "Weight": "3.11 lb"
  },
  "variants": [
    {
      "name": "Actual Color",
      "type": "DROPDOWN",
      "options": [
        {"id": "actual_color-tranquilpink", "name": "Tranquil pink", "availability": "AVAILABLE", "itemIds": ["6G9VW0QQAI2X"]},
        {"id": "actual_color-waterfallblue", "name": "Waterfall blue", "availability": "AVAILABLE", "itemIds": ["4QPDNGIGKZZ8"]}
      ]
    }
  ],
  "fulfillmentOptions": [
    {"type": "SHIPPING", "status": "IN_STOCK", "freeShipping": true, "deliveryDate": "2026-07-09T21:59:00.000Z", "fulfillmentBadge": "Tomorrow"},
    {"type": "PICKUP", "status": "IN_STOCK", "freeShipping": true, "deliveryDate": null, "fulfillmentBadge": "Today"},
    {"type": "DELIVERY", "status": "IN_STOCK", "freeShipping": false, "deliveryDate": null, "fulfillmentBadge": "Today"}
  ],
  "returnPolicy": {
    "returnable": true,
    "freeReturns": true,
    "returnWindowDays": 30,
    "returnPolicyText": "Free 30-day returns"
  },
  "reviewSummary": {
    "averageRating": 4.2,
    "totalReviews": 279,
    "ratingBreakdown": {"5": 191, "4": 29, "3": 15, "2": 10, "1": 34},
    "reviewsLookupId": "19X7KSSCUQU5"
  }
}

Error response (when extraction fails or wrong page):

{"error": true, "message": "No product in __NEXT_DATA__. Ensure the page is a Walmart product detail page (walmart.com/ip/...)."}

Success Criteria

itemId is non-null AND title is non-null AND price is non-null OR availability is non-null

Known Limitations

  • wasPrice is null unless the item currently has an active markdown/rollback promotion
  • sellerAverageRating and sellerReviewCount are null for Walmart first-party listings (INTERNAL sellerType)
  • longDescription may be null for items without IDML data (less common)
  • specifications may be empty for items without IDML specifications
  • Variant itemIds are internal product IDs (format: alphanumeric, e.g., "6G9VW0QQAI2X"), not the usItemId; to get the usItemId for a specific variant, navigate to that variant's URL
  • Delivery dates in fulfillmentOptions reflect the browser session's location context (set by the browser's stored zip code)

Execution Efficiency

  • Batch orchestration: Write a bash script to loop through product URLs serially within a single session; do not parallelize within one browser (prone to triggering anti-scraping restrictions). Add 1–2 second intervals between navigations. To increase throughput, open multiple stealth browser sessions and distribute work across them — each session has an independent fingerprint so rate limits apply per session
  • Test before batch execution: After writing a batch script, you must first test with 1-2 items to verify the script runs correctly; only then run the full batch. Never skip testing and execute in batch directly
  • Reduce redundant pre-operations: When multiple steps depend on the same prerequisite state, complete them in batch under that state to avoid repeatedly establishing the same state
  • Error resumption: Save results item by item during batch processing; on failure, resume from the breakpoint rather than starting over

Experience Notes

Path: {working-directory}/browser-act-skill-forge-memories/walmart-scraper-walmart-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 product URLs were scraped or what prices were found — 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