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Linkfox Keepa Product History

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

Query Keepa for Amazon ASIN price, rank, and offer history to judge margin stability, seasonality, and competitive pricing before sourcing or listing a product.

About

The linkfox-keepa-product-history skill equips coding agents to pull Keepa Amazon ASIN histories—prices, ranks, offers, and volatility—and use that data to validate ecommerce opportunities, stress-test margins, and document pricing rationale before integrations or go-live.

  • Fetches Keepa product history for Amazon ASINs
  • Exposes price, sales rank, and offer trend context
  • Supports margin and demand validation before sourcing
  • Agent-ready workflow for marketplace research
  • Turns historical pricing signals into listing decisions

Linkfox Keepa Product History by the numbers

  • 171 all-time installs (skills.sh)
  • +1 installs in the week ending Aug 2, 2026 (Skillselion tracking)
  • Ranked #620 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-keepa-product-history

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Listed on Skillselion
Installs171
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Query Keepa for Amazon ASIN price, rank, and offer history to judge margin stability, seasonality, and competitive pricing before sourcing or listing a product.

Files

SKILL.mdMarkdownGitHub ↗

Keepa Product Time-Series Data Explorer

This skill guides you on how to query and analyze Amazon product historical time-series data, helping Amazon sellers track price movements, BSR trends, rating changes, and other key product metrics over time.

Core Concepts

This tool provides historical time-series data for individual Amazon products (ASINs) powered by Keepa. It returns timestamped data points for various metrics, allowing trend analysis over a configurable time window (up to 365 days). Each query targets a single ASIN in a specific Amazon marketplace.

Time-series format: All data series are returned as arrays of {time, value} objects, where time is a timestamp and value is the metric at that point. BSR data includes a categoryName field along with a points array.

BSR logic: A smaller BSR value means a better sales rank. Rank 1 is the top-selling product in its category. When a user says "BSR improved", it means the numeric value decreased; "BSR dropped" means the value increased.

Available Data Series

SeriesParameterDescription
Buy Box Price(always returned)Buy Box price over time
Lowest New PriceshowPrice=1Lowest marketplace new item price
List PriceshowPriceList=1Strikethrough / list price
Deal PriceshowPriceDeal=1Lightning deal price
Prime Exclusive PriceshowPricePrime=1Prime-exclusive new item price
FBA PriceshowPriceFba=1Third-party FBA new item price
FBM PriceshowPriceFbm=1Third-party FBM new item price
Coupon PriceshowPriceCoupon=1Post-coupon Buy Box price
Main Category BSRshowBsrMain=1Best Sellers Rank in the main (root) category
Seller CountshowSellerCount=1Number of active sellers
Rating(always returned)Product star rating over time
Rating Count(always returned)Number of ratings over time
Monthly Sales(always returned)Monthly unit sales volume
Sub-category BSR(always returned)Best Sellers Rank in sub-categories

Supported Marketplaces

Domain IDMarketplace
1Amazon.com (US)
2Amazon.co.uk (UK)
3Amazon.de (Germany)
4Amazon.fr (France)
5Amazon.co.jp (Japan)
6Amazon.ca (Canada)
8Amazon.it (Italy)
9Amazon.es (Spain)
10Amazon.in (India)
11Amazon.com.mx (Mexico)
12Amazon.com.br (Brazil)

Default marketplace is 1 (US). Use domain=1 when the user does not 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/keepa_product_history.py directly to run queries.

Parameter Guide

Required Parameters

  • asin: The Amazon Standard Identification Number to query. Only a single ASIN per request is supported.
  • domain: The Amazon marketplace domain ID (see table above). Always map the user's marketplace mention to the correct numeric ID.

Optional Parameters

  • days: Number of historical days to retrieve (1-365, default 90). Use 30 for short-term, 90 for medium-term, 365 for long-term analysis.
  • *show\ flags*: Set any `show parameter to 1` to include that data series. By default, only the core series (Buy Box price, rating, rating count, monthly sales, sub-category BSR) are returned.

How to Choose Parameters

1. Price analysis: Enable showPrice, showPriceList, showPriceDeal, showPriceCoupon as needed for the specific price comparison the user wants. 2. FBA vs FBM comparison: Enable both showPriceFba and showPriceFbm. 3. BSR deep-dive: Enable showBsrMain to get the root category BSR alongside the always-returned sub-category BSR. 4. Competitive landscape: Enable showSellerCount to see how many sellers are competing. 5. Full product overview: Enable all show flags for a comprehensive historical snapshot.

Usage Examples

1. Basic price history for a US product

asin: B0XXXXXXXX, domain: 1, days: 90

2. Long-term BSR trend (1 year) on the German marketplace

asin: B0XXXXXXXX, domain: 3, days: 365, showBsrMain: 1

3. Price comparison across fulfillment channels

asin: B0XXXXXXXX, domain: 1, days: 30, showPriceFba: 1, showPriceFbm: 1, showPrice: 1

4. Deal and coupon price tracking

asin: B0XXXXXXXX, domain: 1, days: 90, showPriceDeal: 1, showPriceCoupon: 1

5. Full product health check

asin: B0XXXXXXXX, domain: 1, days: 90, showPrice: 1, showPriceList: 1, showPriceDeal: 1, showPricePrime: 1, showPriceFba: 1, showPriceFbm: 1, showPriceCoupon: 1, showBsrMain: 1, showSellerCount: 1

Display Rules

1. Present data clearly: Show time-series data in tables or describe trends; avoid subjective business advice unless the user explicitly asks for it. 2. BSR clarification: When showing BSR data, remind users that lower values mean better (higher) sales ranks. 3. Price formatting: Display prices with proper currency symbols matching the marketplace ($ for US, EUR for DE/FR/ES/IT, GBP for UK, JPY for JP, etc.). 4. Time formatting: Present timestamps in a human-readable date format. 5. Trend summarization: When data series are long, summarize the overall trend (e.g., "price decreased from $29.99 to $24.99 over 90 days") and highlight significant changes such as price drops, BSR spikes, or rating shifts. 6. Error handling: When a query fails, explain the reason and suggest corrections (e.g., verify the ASIN is valid, check the marketplace domain ID). 7. Single ASIN limitation: If the user asks about multiple ASINs, inform them that queries must be made one ASIN at a time, and run multiple sequential calls.

Important Limitations

  • Single ASIN per query: Only one ASIN can be queried at a time. For multi-ASIN comparisons, make separate requests.
  • Maximum 365 days: Historical data is limited to at most 365 days back.
  • Data granularity: Data points are at irregular intervals depending on when Keepa captured changes, not at fixed daily intervals.

User Expression & Scenario Quick Reference

Applicable -- Historical product-level data queries on Amazon:

User SaysScenario
"What's the price history for this ASIN"Price trend analysis
"Show me the BSR trend", "how is it ranking"BSR tracking
"Has the price dropped recently", "any deals"Price drop / deal detection
"How many sellers are on this listing"Seller count trend
"What's the rating trend", "review count over time"Rating / review tracking
"FBA vs FBM price", "who has the Buy Box"Fulfillment price comparison
"Monthly sales for this product"Sales volume trend
"Was there a price war on this ASIN"Competitive pricing analysis
"Show me the Keepa chart", "Keepa data"Explicit Keepa data requests

Not applicable -- Needs beyond product-level historical data:

  • Search term / keyword analysis (use ABA data instead)
  • Advertising / PPC campaign data
  • Listing copywriting or content optimization
  • Category-wide or market-level aggregate trends (this tool is per-ASIN only)
  • Real-time inventory or stock level checks
  • Product reviews text or sentiment analysis

Boundary judgment: When users say "product research" or "competitor analysis", if it boils down to examining a specific ASIN's historical price, BSR, or sales data, this skill applies. If they need keyword data, market-wide trends, or advertising metrics, 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/keepa_product_history.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

Automation & Workflowsecommercepricing

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