/price-comparison

name: price-comparison
description: >
  Shopping price comparison using Bright Data's web scraping infrastructure.
  Finds where a product is sold, for how much, and whether it's in stock —
  across Amazon, Walmart, eBay, Best Buy, Google Shopping, and any retailer
  URL — then ranks the offers into a single buy-recommendation table. Use this
  skill when the user wants to compare prices, find the cheapest place to buy
  something, do a price check, see "how much does X cost on Amazon vs Walmart",
  track an item's price, or decide where to buy a product. Handles product
  names, ASINs, and direct URLs, and is region-aware (country affects price,
  availability, and which retailers apply). This is consumer purchase-decision
  research — for analyzing a competitor's pricing *strategy*, use
  competitive-intel instead.

Price Comparison

Find the best place to actually buy a product — lowest price, in stock, from a reputable seller — using live retailer data, not stale training knowledge. Combines the Bright Data CLI (`bdata`) for collection with a normalization + ranking layer to deliver a single cited comparison table and a clear buy recommendation.

**Never quote prices from training knowledge.** Prices and stock change hourly. Always pull live data first, then compare. If a source fails, say so — never fill a price gap with a guess.

Prerequisites

1. Bright Data CLI installed:

   curl -fsSL https://cli.brightdata.com/install.sh | bash

2. One-time login completed:

   bdata login    # or: bdata login --device  (SSH / headless)

Verify before collecting:

if ! command -v bdata >/dev/null 2>&1; then
    echo "bdata CLI not installed — see skills/bright-data-best-practices/references/cli-setup.md"
elif ! bdata zones >/dev/null 2>&1; then
    echo "bdata not authenticated — run: bdata login"
fi

Halt and route to setup if either check fails.

Core Workflow

1. **Clarify scope** — *What* product (name, ASIN, or URL)? *Which* retailers (default: Amazon + Google Shopping)? *Which* country/region (default: US — it changes price, currency, availability, and which retailers apply)? What matters beyond price (reviews, shipping/Prime, new vs refurbished)? 2. **Resolve, then collect** — If you only have a product name, use `amazon_product_search` and `bdata search --type shopping` to resolve it to concrete product URLs/offers, *then* pull each retailer's structured data. Parallelize independent calls. 3. **Normalize** — Collapse every result into the single offer schema in [references/output-and-pricing.md](references/output-and-pricing.md) before comparing. Convert all prices to one currency and note the rate + date used. 4. **Rank & flag** — Sort by total landed cost (price + shipping). Flag out-of-stock, refurbished/used, and third-party-seller offers — a lower price that's unavailable or used is not the winner by default. 5. **Deliver** — Produce the comparison table (Output A), then the explicit "Best buy" recommendation. Every report names the cheapest *in-stock* option and any meaningful trade-offs.

Data Collection Rules

• **Resolve names to URLs first.** You rarely have clean URLs up front. Use

`amazon_product_search "<query>" "https://www.amazon.com"` and `bdata search "<product>" --type shopping --json` to find the exact items, *then* feed those URLs to product pipelines.

• **Prefer pipelines over scraping for supported retailers.** Amazon, Walmart,

eBay, Best Buy, Google Shopping all have structured pipelines that return clean price/availability/rating JSON. Never `bdata scrape amazon.com` — Amazon blocks scrapers; the pipeline bypasses that reliably.

• **Always pass `--json`** when you need to parse or compare output.
• **Be cost-efficient** — a standard comparison is ~3–8 `bdata` calls, not 50.

Pull the offers the user asked about, not every seller on the page.

• **Parallelize** independent calls across multiple Bash tool calls in one

response — don't wait for Amazon before starting Walmart.

• **Every price needs a source URL and a collection timestamp.** No

unattributed or undated prices, ever.

• **Never fabricate a price or fill gaps.** If a retailer returns nothing,

report it in "Gaps & caveats".

Retailer Modules

Pick the retailers that fit the product and region. US electronics → Amazon + Best Buy + Walmart + Google Shopping; marketplace/used → eBay; non-US → confirm the local Amazon domain and add region-relevant retailers.

Amazon — by URL or ASIN

bdata pipelines amazon_product "https://www.amazon.com/dp/<ASIN>" --json -o amazon.json

Returns price, `final_price`, title, availability, rating, review count, ASIN, seller, images. Use the right domain for the region (`amazon.com`, `amazon.de`, `amazon.co.uk`, …).

Amazon — discover by keyword (when you only have a name)

bdata pipelines amazon_product_search "iPhone 17 Pro 256GB" "https://www.amazon.com" --json -o amzn_search.json

Resolve the right ASIN/URL from the results, then call `amazon_product` on it.

Walmart / eBay / Best Buy — by product URL

bdata pipelines walmart_product "https://www.walmart.com/ip/<ID>" --json -o walmart.json
bdata pipelines ebay_product     "https://www.ebay.com/itm/<ID>"   --json -o ebay.json
bdata pipelines bestbuy_products "https://www.bestbuy.com/site/<ID>.p" --json -o bestbuy.json

Google Shopping — cross-retailer overview

bdata pipelines google_shopping "<google-shopping-product-url>" --json -o gshopping.json

Best for a fast multi-seller view once you have a Shopping product URL. To *find* that URL (and a quick price spread) from a name, use SERP shopping:

bdata search "iPhone 17 Pro 256GB" --type shopping --country us --json

Unknown / local retailer — scrape the page

bdata scrape "https://retailer.example/product-page"

Then extract price, currency, and stock from the markdown. Use