Walmart Product Reviews

Walmart Product Reviews

Fetch Walmart product reviews through Frevana.

Purpose

This skill is for retrieving reviews for one known Walmart product.

Inputs:

• product_id
• optional page
• optional sort
• optional rating

Output:

• validated response JSON with Walmart product review results
• the same validated JSON saved to a local file on every successful run

The required product_id is the Walmart us_item_id from Walmart Search results or Walmart Product results. This skill validates that the response is JSON, saves it to disk, and returns it unchanged on stdout. Do not rewrite or reshape the returned data unless the user explicitly asks for a transformation.

What This Skill Needs

• user-provided product_id / Walmart us_item_id
• optional page number
• optional sort value
• optional rating filter
• FREVANA_TOKEN in the environment variables, or an explicit --token override for the current run
• curl
• bash
• python3

Execution Order

Use this flow so the request stays simple and reliable:

1. Confirm the user has provided product_id or an equivalent Walmart us_item_id.
2. If the user gives only a product name, search query, or generic Walmart URL without a clear item id, do not guess. Suggest running walmart-search first to obtain an organic_results[].us_item_id.
3. Prefer the script over ad hoc curl commands.
4. Let the script read FREVANA_TOKEN first.
5. In interactive shell usage, if FREVANA_TOKEN is missing, the script may prompt for it.
6. In non-interactive or agent workflows, fail fast if the token is missing and tell the user to set FREVANA_TOKEN or pass --token explicitly.
7. Run the script once. It prints the validated JSON to stdout and saves the same JSON to a file.
8. Use the saved JSON file for any follow-up parsing or summarization instead of calling the reviews API again.
9. Return the validated response JSON, or summarize the product, overall rating, rating counts, top positive/negative review, individual reviews, and follow-up pagination when available.

Commands

Basic review lookup

bash <skill-path>/scripts/search_walmart_product_reviews.sh \
  --product-id 5689919121

--product_id, --us-item-id, and --us_item_id are accepted as aliases for --product-id.

Review lookup with filters

bash <skill-path>/scripts/search_walmart_product_reviews.sh \
  --product-id 5689919121 \
  --rating 5 \
  --sort submission-desc \
  --page 2

Save response JSON to a specific file

bash <skill-path>/scripts/search_walmart_product_reviews.sh \
  --product-id 5689919121 \
  --output ./out/walmart-product-reviews-result.json

Token override for the current run

bash <skill-path>/scripts/search_walmart_product_reviews.sh \
  --product-id 5689919121 \
  --token "your bearer token"

Fixed Request Shape

The script sends this payload shape, omitting optional fields that were not provided:

{
  "product_id": "5689919121",
  "page": 2,
  "sort": "submission-desc",
  "rating": 5
}

Only product_id is required. Do not invent product_id, page, sort, or rating values when the user did not provide them.

The Frevana endpoint schema currently exposes only product_id, page, sort, and rating; do not pass unsupported passthrough fields such as engine, api_key, output, no_cache, async, or zero_trace..

Response Shape

The API returns Frevana JSON. Common fields include:

• search_metadata
• search_parameters
• product
• overall_rating
• total_count
• ratings
• top_positive
• top_negative
• reviews
• pagination

Review entries commonly include position, title, text, rating, positive_feedback, negative_feedback, review_submission_time, user_nickname, and customer_type.

Output

• Success: the script validates that the response body is JSON, writes it to a file, prints the saved path to stderr, and prints the JSON to stdout
• Default file path: ./out/walmart-product-reviews-<UTC timestamp>-<pid>.json
• With --output: the same JSON is written to the specified file path instead of the default path
• Failure: the script prints the response body or parsing error and exits non-zero

Notes

• Require --product-id, --product_id, --us-item-id, or --us_item_id.
• Use --page only when the user requests a review page; it must be an integer >= 1.
• Use --sort only when the user specifies review sorting. Accepted values are relevancy, helpful, submission-desc, submission-asc, rating-desc, and rating-asc.
• Use --rating only when the user requests a star filter. Accepted values are integers from 1 to 5.
• If the user only has a product search query, run walmart-search first and use the chosen result's us_item_id as product_id.
• If curl is missing, stop and tell the user to install curl.
• If python3 is missing, stop and tell the user to install python3.
• Do not echo the Bearer token back to the user.
• Summarize review title, text, rating, date, user nickname, customer type, positive/negative feedback, and pagination when available unless the user asks for the full payload.

Example Prompts

中文

• "查 Walmart product_id=5689919121 的评论"
• "获取 Walmart us_item_id 5689919121 的 1 星评论"
• "查这个 Walmart 商品评论，sort=submission-desc，page=2"
• "我只有 coffee maker 关键词" -> 先用 walmart-search 获取 us_item_id

English

• "Get Walmart product reviews for product_id 5689919121."
• "Fetch 5-star Walmart reviews sorted by submission-desc for this us_item_id."
• "Call the Frevana walmart-product-reviews endpoint and save the raw JSON."
• "Find reviews for 'coffee maker'." -> Explain that a Walmart product_id / us_item_id is required, and suggest running Walmart search first.