Walmart Product Detail logo

Walmart Product Detail

OrganizationPopular
browser-act
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.

Overview

Publisherbrowser-act
Repositoryskills
Skill namewalmart-product-detail
Stars
5.9K
Forks
295
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by browser-act on GitHub. Read the source before you install it.

Installation

Install the Walmart Product Detail AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/browser-act/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/solutions/ecommerce/walmart-product-detail .claude/skills/walmart-product-detail
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Walmart Product Detail in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Walmart Product Detail on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Walmart Product Detail is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

json
{
  "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):

json
{"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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Walmart Product Detail AI skill do?

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 produc...

Why use Walmart Product Detail on TypingMind?

Because you install it once and use it with any model. Walmart Product Detail is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Walmart Product Detail in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/browser-act/skills/tree/main/solutions/ecommerce/walmart-product-detail. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Walmart Product Detail?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Walmart Product Detail?

As many as you like. As long as a model supports skills, you can use Walmart Product Detail with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Walmart Product Detail AI skill free?

Yes. It is published on GitHub by browser-act under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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