Walmart Product Sellers

Fetch Walmart product seller offers through Frevana.

Purpose

This skill is for retrieving seller offers for one known Walmart product.

Inputs:

• product_id
• optional store_id

Output:

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

The required productid is the Walmart usitem_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 productid / Walmart usitem_id
• optional Walmart store_id
• 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 productid or an equivalent Walmart usitem_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 organicresults[].usitem_id.
3. Use store_id only when the user explicitly provides a Walmart store ID.
4. Prefer the script over ad hoc curl commands.
5. Let the script read FREVANA_TOKEN first.
6. In interactive shell usage, if FREVANA_TOKEN is missing, the script may prompt for it.
7. 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.
8. Run the script once. It prints the validated JSON to stdout and saves the same JSON to a file.
9. Use the saved JSON file for any follow-up parsing or summarization instead of calling the sellers API again.
10. Return the validated response JSON, or summarize the product, shipping destination, sellers, prices, delivery, return policy, availability, seller type, and store-front links when available.

Commands

Basic seller lookup

bash <skill-path>/scripts/search_walmart_product_sellers.sh \
  --product-id 10543894

--productid, --us-item-id, and --usitem_id are accepted as aliases for --product-id.

Store-specific seller lookup

bash <skill-path>/scripts/search_walmart_product_sellers.sh \
  --product-id 10543894 \
  --store-id 5888

--store_id is accepted as an alias for --store-id.

Save response JSON to a specific file

bash <skill-path>/scripts/search_walmart_product_sellers.sh \
  --product-id 10543894 \
  --output ./out/walmart-product-sellers-result.json

Token override for the current run

bash <skill-path>/scripts/search_walmart_product_sellers.sh \
  --product-id 10543894 \
  --token "your bearer token"

Fixed Request Shape

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

{
  "product_id": "10543894",
  "store_id": "5888"
}

Only productid is required. Do not invent productid or store_id values when the user did not provide them.

The Frevana endpoint schema currently exposes only productid and storeid; do not pass unsupported passthrough fields such as engine, apikey, output, nocache, async, or zero_trace..

Response Shape

The API returns Frevana JSON. Common fields include:

• search_metadata
• search_parameters
• sellers_results

The sellersresults object commonly includes product, shippingdestination, and sellers. Seller entries commonly include position, usitemid, offerid, offertype, availabilitystatus, sellerid, sellername, sellerdisplayname, sellertype, sellerstorefronturl, price, extractedprice, returnpolicytext, deliverydate, deliveryprice, and extracteddeliveryprice.

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-sellers-<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, --productid, --us-item-id, or --usitem_id.
• Use --store-id only when the user provides a Walmart store ID.
• If the user only has a product search query, run walmart-search first and use the chosen result's usitemid 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 product name, shipping destination, seller name, seller type, availability, offer type, price, delivery date, delivery price, return policy, and seller store-front URL unless the user asks for the full payload.

Example Prompts

中文

• "查 Walmart product_id=10543894 的 sellers"
• "获取 Walmart usitemid 10543894 在 store_id=5888 的卖家报价"
• "查这个 Walmart 商品有哪些第三方卖家"
• "我只有 coffee maker 关键词" -> 先用 walmart-search 获取 usitemid

English

• "Get Walmart product sellers for product_id 10543894."
• "Fetch Walmart seller offers for this usitemid and store_id 5888."
• "Call the Frevana walmart-product-sellers endpoint and save the raw JSON."
• "Find sellers for 'coffee maker'." -> Explain that a Walmart productid / usitem_id is required, and suggest running Walmart search first.