walmart-product-reviews skill

by browser-act·browser-act/skills·6.0k stars·MIT

Walmart product reviews scraper: given a walmart.com product item ID, navigate to the reviews page and extract paginated customer reviews including reviewId, rating, title, review text, author nickname, submission date, verified purchase status, helpful votes, variant selected (color/size), badges, fulfilled by, seller name, and photo count. Use when user mentions walmart reviews, walmart product reviews, walmart customer reviews, scrape walmart reviews, extract walmart reviews, walmart review scraper, walmart ratings and reviews, walmart review data, walmart review text, walmart review pagination, get reviews from walmart, walmart review collector, walmart verified purchase reviews, walmart review analysis, walmart sentiment analysis, walmart review export, walmart buyer feedback. Also applies to bulk collection of walmart product reviews across multiple items, sentiment analysis on walmart reviews for market research, competitor product review benchmarking on walmart, and monitoring new walmart reviews over time.

A97/100content scan

Is the walmart-product-reviews skill safe?

Clean: nothing in its files matched our rules. We read 2 files in the folder on 2026-09-28.

lowSKILL.md:1
The description is over 1,024 characters, the limit agents read.
```
1030 characters
```

Install the walmart-product-reviews skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

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-reviews ~/.claude/skills/walmart-product-reviews

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Walmart — Product Reviews

product item ID + page → paginated customer reviews from walmart.com

Language

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

Objective

Extract paginated customer reviews from a Walmart product reviews page, returning structured review data with ratings, text, author info, and metadata.

Prerequisites

Target reviews page is open in the browser: https://www.walmart.com/reviews/product/{item-id}?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 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 reviews from current reviews page

Navigate to the target reviews URL first, then extract:

navigate "https://www.walmart.com/reviews/product/{item-id}?page={page}"
wait stable
eval "$(python scripts/extract-reviews.py)"

Parameters in URL:

{item-id}: Walmart item ID (numeric, e.g., 18656507313)
{page}: page number starting from 1; 10 reviews per page

Note: The walmart-product-detail Skill also returns the first 10 reviews in its reviewSummary.reviewsLookupId field along with numberOfReviews. Use these to determine total pages before starting pagination.

Output example:

{
  "totalReviews": 279,
  "averageRating": 4.2,
  "reviewsOnPage": 10,
  "ratingBreakdown": {
    "5": 191,
    "4": 29,
    "3": 15,
    "2": 10,
    "1": 34
  },
  "lookupId": "19X7KSSCUQU5",
  "reviews": [
    {
      "reviewId": "431060075",
      "rating": 5,
      "title": null,
      "text": "Purchased for adult daughter's bday! She loves it...",
      "author": "kimberly",
      "submittedDate": "7/4/2026",
      "verifiedPurchase": true,
      "helpfulVotes": 0,
      "notHelpfulVotes": 0,
      "variantSelected": {"Color": "Tranquil pink"},
      "badges": ["Verified Purchase"],
      "fulfilledBy": "Walmart",
      "sellerName": "Walmart.com",
      "media": null
    }
  ]
}

Error response (when extraction fails or wrong page):

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

Pagination

URL Pagination: URL pattern https://www.walmart.com/reviews/product/{item-id}?page={N}. Start at page 1. Increment page by 1 each iteration. Termination: reviewsOnPage === 0 OR page > ceil(totalReviews / 10). Each page returns 10 reviews.

Success Criteria

reviewsOnPage >= 1 AND reviews[0].reviewId is non-null AND reviews[0].rating is a number between 1 and 5

Known Limitations

10 reviews per page; Walmart does not expose an API to change page size
title is null for most reviews that do not have a title
media (photo URLs) is null for most text-only reviews; photo URLs are not included in NEXTDATA__ for reviews with photos — only a count is available
variantSelected is null when the reviewer did not select a specific variant
Review ordering defaults to most recent; sort order cannot be changed via URL parameter

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 pages 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 page by page 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-reviews.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 products were reviewed or what ratings were found — those are task outputs, not experience.

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