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Linkfox Sellersprite Competitor

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

Research Amazon marketplace competitors via SellerSprite-style signals—ASIN overlap, keyword gaps, pricing bands, and listing patterns—before launching or repositioning a product.

About

Guides Claude through SellerSprite-style Amazon seller competitor research: identifying rival ASINs, comparing keywords, prices, reviews, and listing structure so teams can validate niches and craft differentiation before sourcing or launching SKUs.

  • SellerSprite-oriented competitor workflows
  • Amazon ASIN and keyword gap analysis
  • Pricing and listing pattern comparison
  • Niche validation before inventory commits
  • Positioning inputs for go-to-market

Linkfox Sellersprite Competitor by the numbers

  • 170 all-time installs (skills.sh)
  • Ranked #357 of 853 Sales & Marketing 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-sellersprite-competitor

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Listed on Skillselion
Installs170
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Research Amazon marketplace competitors via SellerSprite-style signals—ASIN overlap, keyword gaps, pricing bands, and listing patterns—before launching or repositioning a product.

Files

SKILL.mdMarkdownGitHub ↗

SellerSprite Competitor Lookup

This skill guides you on how to query and analyze Amazon competitor product data, helping Amazon sellers discover competing products, benchmark performance, and extract actionable competitive intelligence.

Core Concepts

The SellerSprite Competitor Lookup tool provides comprehensive Amazon product data across 12 marketplaces. It allows querying products by ASIN, keyword, seller name, brand, or category, and returns detailed metrics including monthly sales volume, revenue, BSR ranking, pricing, ratings, and growth trends.

Data snapshots: The tool supports both real-time data (last 30 days) and historical monthly snapshots. Use nearly (default) for current data or a yyyyMM format (e.g., 202501) for historical snapshots. Historical snapshots capture all active listings for that month, enabling year-over-year and seasonal comparisons.

Category hierarchy: Amazon category names support multi-level paths separated by colons (:). For example, Electronics:Computers & Accessories:Monitors. Convert user-provided category descriptions into the proper colon-separated format.

Supported Marketplaces

US (United States), UK (United Kingdom), DE (Germany), FR (France), JP (Japan), CA (Canada), IT (Italy), ES (Spain), MX (Mexico), AU (Australia), TR (Turkey), IN (India)

Default marketplace is US. Use US when the user does not specify a marketplace.

Parameter Guide

Search Filters

ParameterDescriptionExample
marketplaceAmazon marketplace codeUS, UK, DE, JP
keywordSearch keyword (translate to the marketplace language)wireless earbuds
asinListOne or more ASINs, comma-separated (max 40)B072MQ5BRX,B08N5WRWNW
sellerNameSeller name to filter byAnker Direct
brandBrand name to filter byAnker
nodeLabelAmazon category name (colon-separated levels)Electronics:Headphones
nodeIdPathAmazon category ID path172282
matchTypeKeyword match mode: 1 = phrase, 2 = fuzzy, 3 = exact (default 1)1
showVariationShow product variations: Y or N (default N)N
dataSnapshotMonthData snapshot month (nearly for real-time, or yyyyMM)nearly

Pagination & Sorting

ParameterDescriptionExample
pagePage number, starting from 11
sizeResults per page, 10-100 (default 50)50
order.fieldSort field (see sort options below)total_units
order.descSort direction: true = descending, false = ascendingtrue

Sort Field Options

FieldDescription
total_unitsMonthly sales units
total_amountMonthly sales revenue
bsr_rankBSR ranking
pricePrice
ratingRating score
reviewsNumber of reviews
profitGross margin
reviews_rateReview rate
available_dateListing date
questionsQ&A count
total_units_growthMonthly sales unit growth rate
total_amount_growthMonthly revenue growth rate
reviews_increasementMonthly new reviews
bsr_rank_cv7-day BSR growth count
bsr_rank_cr7-day BSR growth rate
amz_unitVariant sales units

Key Response Fields

FieldDescription
asinProduct ASIN
titleProduct title
priceCurrent price
monthlySalesUnitsMonthly sales volume
monthlySalesRevenueMonthly sales revenue
bsrBSR ranking
bsrGrowthRateBSR growth rate
bsrGrowthCountBSR growth count
ratingRating score
ratingsNumber of ratings
ratingsGrowthMonthly new ratings
ratingsRateReview rate
brandBrand name
sellerNameBuyBox seller
sellerNationBuyBox seller nationality
fulfillmentFulfillment type (AMZ/FBA/FBM)
availableDateStringListing date
profitGross margin
nodeLabelPathCategory path
imageUrlProduct image URL
monthlySalesUnitsGrowthRateMonthly sales growth rate
listingQualityScoreListing quality score
variationNumNumber of variations
parentParent ASIN
badgeBestSellerBest Seller badge (Y/N)
badgeAmazonChoiceAmazon's Choice badge (Y/N)
badgeEbcA+ Content (Y/N)
badgeVideoVideo present (Y/N)

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/sellersprite_competitor_lookup.py directly to run queries.

Usage Examples

1. Look up competitors by ASIN

{
  "marketplace": "US",
  "asinList": "B072MQ5BRX,B08N5WRWNW"
}

Use case: Analyze specific competing products by their ASINs.

2. Search competitors by keyword

{
  "marketplace": "US",
  "keyword": "wireless earbuds",
  "matchType": 1,
  "order": {"field": "total_units", "desc": "true"},
  "size": 20
}

Use case: Discover top-selling products for a keyword, sorted by monthly sales.

3. Filter by brand and category

{
  "marketplace": "US",
  "brand": "Anker",
  "nodeLabel": "Electronics:Headphones",
  "order": {"field": "total_amount", "desc": "true"}
}

Use case: Analyze a specific brand's product lineup within a category.

4. Find products by seller name

{
  "marketplace": "DE",
  "sellerName": "Anker Direct",
  "order": {"field": "bsr_rank", "desc": "false"}
}

Use case: View all products from a particular seller sorted by BSR.

5. Historical snapshot comparison

{
  "marketplace": "US",
  "keyword": "space heater",
  "dataSnapshotMonth": "202412",
  "order": {"field": "total_units", "desc": "true"},
  "size": 20
}

Use case: Analyze seasonal product performance using historical data snapshots.

6. Show product variations

{
  "marketplace": "JP",
  "asinList": "B0XXXXXXXXX",
  "showVariation": "Y"
}

Use case: Examine all variation-level data for a product family.

Display Rules

1. Present data clearly: Show query results in well-formatted tables. Include key metrics such as ASIN, title, price, monthly sales, BSR, rating, and brand. Do not provide subjective business advice unless the user asks for it. 2. Keyword language: When searching by keyword, always translate the keyword to the target marketplace language (e.g., English for US/UK, German for DE, Japanese for JP). Remind the user of this if they provide keywords in the wrong language. 3. BSR clarification: When displaying BSR data, remind users that a lower BSR value indicates stronger sales performance. 4. Growth metrics: When showing growth rates, clarify whether positive values mean improvement or decline (positive BSR growth count means BSR increased, which means worsened ranking). 5. Pagination notice: When the total result count exceeds the returned page size, inform the user of the total count and offer to fetch additional pages. 6. Badge highlights: When products carry badges (Best Seller, Amazon's Choice, A+ Content, Video), highlight these in the results as they are important competitive signals. 7. Error handling: When a query fails, explain the reason based on the message field and suggest adjusting query parameters. 8. Snapshot guidance: When users want to do seasonal or trend analysis, proactively suggest using historical snapshots (e.g., last year's same month) for comparison.

Important Limitations

  • Result cap: Each page returns 10-100 records (controlled by size). Use pagination for larger result sets.
  • ASIN limit: A maximum of 40 ASINs can be queried at once via asinList.
  • Historical snapshots: Only existing monthly snapshots can be queried; future dates are not supported.
  • Keyword language: Keywords should match the marketplace language for best results.

User Expression & Scenario Quick Reference

Applicable -- Amazon competitor product data queries:

User SaysScenario
"Find competitors for this ASIN"ASIN-based competitor lookup
"Top sellers for wireless earbuds"Keyword-based product discovery
"What is this seller selling"Seller product portfolio analysis
"Show me products in Electronics category"Category-based browsing
"Monthly sales for these ASINs"Sales estimation for specific products
"New products gaining traction"Growth trend detection
"Compare products across brands"Brand benchmarking
"How was this niche last December"Historical snapshot analysis
"Best sellers with high ratings"Multi-metric filtering
"FBA vs FBM in this category"Fulfillment type analysis

Not applicable -- Needs beyond competitor product data:

  • ABA search term data or keyword ranking (use ABA Data Explorer instead)
  • Advertising / PPC campaign management
  • Product reviews content or sentiment analysis
  • Listing copywriting or optimization suggestions
  • Supplier sourcing or manufacturing costs
  • Account health or policy compliance

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/sellersprite_competitor_lookup.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/).

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