
Walmart Category Listing
FreeEfficiently scrape product listings from Walmart categories.
Free · Opens the source repo
What Walmart Category Listing does
The Walmart Category Listing skill is designed to help users extract structured product data from any Walmart category or browse page URL. By providing the specific category URL and an optional page number, users can obtain a comprehensive list of products available in that category. This skill is particularly useful for developers and data analysts who need to gather product information for research, price monitoring, or inventory management.
When executed, the skill navigates to the specified Walmart category page, waits for the page to stabilize, and then runs a Python script that extracts product details such as item ID, title, brand, price, rating, availability, and seller information. The output is returned in a structured JSON format, making it easy to parse and utilize in various applications. The skill supports pagination, allowing users to extract data from multiple pages of listings efficiently.
This skill operates within the constraints of the Walmart website, meaning it only accesses data that is publicly available on the page. It does not bypass any authentication or access controls, ensuring compliance with Walmart's terms of service. Users can also leverage this skill to scrape filtered category URLs directly, enhancing its versatility for specific product searches.
Ideal for developers, data scientists, and e-commerce professionals, this skill streamlines the process of collecting product data, saving time and effort compared to manual extraction methods. By automating the scraping process, users can focus on analyzing the data rather than spending hours gathering it.
When to use it
Use this skill when you need to gather product information from Walmart category pages for analysis or monitoring.
When not to use it
This skill is not suitable for scraping data from pages that require authentication or for accessing private data on Walmart's site.
What you can build with it
Market Research
Use the skill to gather product data from Walmart for competitive analysis or market research.
Price Monitoring
Set up a script to regularly scrape product prices and track changes over time for specific categories.
Inventory Management
Extract product listings to maintain an up-to-date inventory of items available in Walmart's categories.
How to install Walmart Category Listing
View source1. Install with the skills CLI
npx skills add browser-act/skills/walmart-category-listing --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by browser-actWalmart — Category Listing
category URL + page → paginated product list from walmart.com browse/category page
Language
All process output to user (progress updates, process notifications) follows the user's language.
Objective
Extract product listings from any Walmart category or browse page URL, returning structured item data with pricing, rating, availability, and seller info.
Prerequisites
- Target category page is open in the browser:
https://www.walmart.com/browse/{category-slug}/{category-ids}?page={page}
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 viaeval "$(python scripts/xxx.py {params})".$(...)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 product listing from current category page
Navigate to the target category URL first, then extract. Category URLs may include filter parameters copied from the browser.
navigate "{category_url}?page={page}"— if the URL already has query params, use&page={page}insteadwait stableeval "$(python scripts/extract-listing.py)"
URL format examples:
https://www.walmart.com/browse/home/?page=1https://www.walmart.com/browse/auto-tires/brake-pads/91083_1074765_9038935_4582920?page=1https://www.walmart.com/cp/1149374?page=2(category ID URL)- With filters:
https://www.walmart.com/browse/electronics/laptops?minPrice=500&maxPrice=1000&page=1
Output example:
{
"pageType": "BrowsePage",
"query": null,
"currentPage": 1,
"totalCount": 60010,
"maxPage": 25,
"itemCount": 51,
"items": [
{
"itemId": "2830965432",
"url": "https://www.walmart.com/ip/Product-Name/2830965432",
"title": "Product title here",
"brand": "Brand Name",
"image": "https://i5.walmartimages.com/seo/product.jpeg",
"price": 19.99,
"priceString": "$19.99",
"wasPrice": 24.99,
"rating": 4.5,
"reviewCount": 1234,
"availability": "IN_STOCK",
"availabilityText": "In stock",
"sellerName": "Walmart.com",
"sellerType": null,
"fulfillmentBadge": null,
"classType": "REGULAR",
"shortDescription": null
}
]
}
Error response (when extraction fails or wrong page):
{"error": true, "message": "No searchResult in __NEXT_DATA__. Ensure the page is fully loaded at the correct search URL."}
Pagination
URL Pagination: Append ?page={N} (or &page={N} if URL has existing query params) to the category URL. Increment page by 1 each iteration. Termination: page > maxPage (from response maxPage field) OR itemCount === 0. Note: Walmart caps category browsing at maxPage pages (up to 25 for broad categories).
Success Criteria
itemCount >= 1 AND items[0].itemId is non-null AND items[0].url starts with https://www.walmart.com/ip/
Known Limitations
- Walmart limits category pagination to at most ~25 pages regardless of total result count
brandfield is null for many items in listing pages (available in product detail)wasPriceis null unless the item has an active markdown/rollback- Heavily filtered category URLs (applied from browser) are directly usable — paste as-is and append
?page=N
Execution Efficiency
- Batch orchestration: Write a bash script to loop through pages serially within a single session; do not parallelize within one browser (prone to triggering anti-scraping restrictions). Add 1–2 second intervals between page 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-category-listing.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 URLs were scraped or how many results were returned — those are task outputs, not experience.
Frequently asked questions about Walmart Category Listing
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