Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
linkfox-ai avatar

Linkfox Amazon Product Detail

  • 261 installs
  • 64 repo stars
  • Updated August 3, 2026
  • linkfox-ai/linkfox-skills

Pull Amazon listing details—title, price, reviews, attributes—to benchmark competitors and shortlist products to sell.

About

LinkFox Amazon product detail skill retrieves structured listing data from Amazon so sellers and agents can analyze competitor ASINs, compare pricing and attributes, and inform early product-selection decisions.

  • Amazon ASIN detail fetch
  • Competitive listing comparison
  • Price and attribute extraction
  • Agent-driven product research

Linkfox Amazon Product Detail by the numbers

  • 261 all-time installs (skills.sh)
  • +42 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #516 of 2,715 Automation & Workflows skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-amazon-product-detail

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs261
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Pull Amazon listing details—title, price, reviews, attributes—to benchmark competitors and shortlist products to sell.

Files

SKILL.mdMarkdownGitHub ↗

Amazon Product Detail Lookup

This skill guides you on how to retrieve and analyze detailed Amazon product information by ASIN, helping Amazon sellers and researchers extract comprehensive listing data from product pages across 22 Amazon marketplaces.

Core Concepts

This tool performs front-end simulation of Amazon product pages to extract structured detail data. It returns rich information including the product title, main image, additional images, bullet points (About This Item), product specifications, A+ content description, pricing, ratings distribution, variant structure, and optionally "Frequently Bought Together" and "Related Products" data.

Billing note: This tool is billed per ASIN queried. Because the cost is higher than search-based tools, guide users to query only the ASINs they truly need rather than large exploratory batches.

Batch support: Up to 40 ASINs can be queried in a single request, provided as a comma-separated string.

Parameter Guide

ParameterRequiredDefaultDescription
asinsYes--Comma-separated ASIN list (up to 40). Example: B072MQ5BRX,B08N5WRWNW
amazonDomainNoamazon.comAmazon marketplace domain. See Supported Marketplaces below
languageNo--Locale code for response language, e.g. en_US, de_DE, ja_JP
deliveryZipNo--Postal/ZIP code for delivery-dependent pricing and availability
deviceNodesktopDevice type: desktop, mobile, or tablet
returnBoughtTogetherNofalseInclude "Frequently Bought Together" products in the response
returnRelatedProductsNofalseInclude "Related Products" list in the response
returnAuthorsReviewsNofalseInclude top customer reviews in the response

Supported Marketplaces

DomainCountry
amazon.comUnited States
amazon.co.ukUnited Kingdom
amazon.deGermany
amazon.frFrance
amazon.itItaly
amazon.esSpain
amazon.co.jpJapan
amazon.caCanada
amazon.com.auAustralia
amazon.com.brBrazil
amazon.inIndia
amazon.nlNetherlands
amazon.seSweden
amazon.plPoland
amazon.sgSingapore
amazon.saSaudi Arabia
amazon.aeUnited Arab Emirates
amazon.com.trTurkey
amazon.com.mxMexico
amazon.egEgypt
amazon.cnChina
amazon.com.beBelgium

Default marketplace is amazon.com (US). Use amazon.com when the user doesn't specify a marketplace.

API Usage

This tool calls the LinkFox tool gateway API. See references/api.md for calling conventions, request parameters, and response structure. You can also execute scripts/amazon_product_detail.py directly to run queries.

Usage Examples

1. Basic single-ASIN lookup

Look up the details of ASIN B072MQ5BRX on Amazon US.

Parameters: {"asins": "B072MQ5BRX"}

2. Multi-ASIN batch lookup

Get product details for B072MQ5BRX and B08N5WRWNW.

Parameters: {"asins": "B072MQ5BRX,B08N5WRWNW"}

3. Lookup on a non-US marketplace

Fetch product info for B09V3KXJPB on Amazon Germany.

Parameters: {"asins": "B09V3KXJPB", "amazonDomain": "amazon.de"}

4. Lookup with reviews and bought-together

Get full product details including reviews and frequently bought together for B08N5WRWNW on Amazon Japan.

Parameters: {"asins": "B08N5WRWNW", "amazonDomain": "amazon.co.jp", "returnBoughtTogether": true, "returnAuthorsReviews": true}

5. Competitor listing comparison

Compare bullet points and pricing for these 3 ASINs: B072MQ5BRX, B08N5WRWNW, B09V3KXJPB.

Parameters: {"asins": "B072MQ5BRX,B08N5WRWNW,B09V3KXJPB"}

6. Mobile-specific product page check

Show me how product B072MQ5BRX looks on mobile in the UK.

Parameters: {"asins": "B072MQ5BRX", "amazonDomain": "amazon.co.uk", "device": "mobile"}

Display Rules

1. Present data clearly: Show product details in a well-structured format -- use tables for specifications and pricing comparisons, bullet lists for "About This Item" content 2. Image handling: When the response includes image URLs (productImageUrls, thumbnail, imageUrl), present them as clickable links or embedded images as appropriate 3. Multi-ASIN results: When multiple ASINs are queried, organize results so each product is clearly separated and labeled by ASIN and title 4. Price formatting: Always include the currency symbol/code alongside price values. Show both current price and original price (if discounted) to highlight deals 5. Rating breakdown: When customerReviews data is present, show the star distribution (5-star through 1-star percentages) alongside the overall rating and total review count 6. Variant display: When variants exist, present them in a compact table grouped by variant dimension (color, size, etc.) 7. Error handling: When a query fails, explain the reason and suggest checking that the ASIN is valid and the marketplace domain is correct 8. Cost awareness: Remind users that this tool charges per ASIN, so they should batch only what they need

User Expression & Scenario Quick Reference

Applicable -- Tasks that require structured Amazon product page data:

User SaysScenario
"Look up this ASIN", "Get product details for ..."Single/batch ASIN detail lookup
"What are the bullet points for this product"Listing content extraction
"Show me competitor listings"Multi-ASIN comparison
"What's the price of this ASIN on Amazon DE"Cross-marketplace price check
"How many reviews does this product have"Rating & review analysis
"What variants does this product offer"Variant structure inspection
"Get the A+ content / product description"Product description retrieval
"What's the main image for this ASIN"Product image extraction
"Is this product Prime eligible"Eligibility / badge check
"What are the product specs / dimensions"Specification lookup

Not applicable -- Needs beyond product detail page data:

  • Keyword / search term analysis (use ABA Data Explorer instead)
  • Search result rankings or organic position tracking
  • Advertising / PPC campaign data
  • Sales estimation or revenue calculations
  • Inventory management or FBA fee analysis
  • Review sentiment analysis requiring NLP beyond raw review text
  • Historical price tracking over time (this tool returns current snapshot only)

Boundary judgment: When users say "analyze this product" or "research this ASIN", if it boils down to retrieving the current product page data (title, price, bullets, images, reviews, variants), this skill applies. If they need historical trends, sales estimates, or advertising insights, it does not apply.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply: 1. The functionality or purpose described in this skill does not match actual behavior 2. The skill's results do not match the user's intent 3. The user expresses dissatisfaction or praise about this skill 4. Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

<!-- LF_LARGE_RESPONSE_BLOCK -->

Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/amazon_product_detail.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"
Pick --out-dir outside any git working tree (e.g. /tmp/... on Unix, %TEMP%/... on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

  • High field count per record, or fields you don't need
  • Batch/paginated results (multiple items per call)
  • Long-text fields (descriptions, reviews, HTML, time series)
  • Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read. <!-- /LF_LARGE_RESPONSE_BLOCK -->

--- For more high-quality, professional cross-border e-commerce skills, set [LinkFox Skills](https://skill.linkfox.com/).

Related skills

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.