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Linkfox Sorftime Product Detail

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

Helps with ai & agent building tasks.

About

linkfox-sorftime-product-detail is a Claude Code skill in the AI & Agent Building category.

  • linkfox-sorftime-product-detail
  • AI & Agent Building
  • AI-coding skill

Linkfox Sorftime Product Detail by the numbers

  • 163 all-time installs (skills.sh)
  • Ranked #3,190 of 16,546 AI & Agent Building 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-sorftime-product-detail

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Listed on Skillselion
Installs163
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Sorftime Product Detail

This skill guides you on how to query Amazon product detail and historical trend data by ASIN via Sorftime, helping Amazon sellers analyze product performance, pricing strategy, and competitive positioning.

Core Concepts

Sorftime Product Detail provides comprehensive product-level data by ASIN, with historical trend data going back to 2021. It covers sales volume & revenue trends, price & promotion tracking, multi-level BSR ranking history, and real-time profit analysis with FBA fee breakdown.

Key differentiator: This tool returns trend/time-series data for individual products. If you need to search/filter products across a category, brand, or seller, use the Sorftime Product Search skill instead.

Data Fields

FieldAPI NameDescriptionExample
Product TitletitleProduct listing titleAnker Portable Charger...
BrandbrandBrand nameAnker
ASINasinAmazon Standard Identification NumberB0088PUEPK
Parent ASINparentAsinParent ASIN if has variations, null otherwiseB0088PUEPK
CategorycategoryMain category [name, NodeId]["Cell Phones", "2811119011"]
Sub-category RankingsbsrCategorySub-category rank list with nodeId, name, rank, date[{nodeId, name, rank, date}]
Listing DateavailableDateListing date (yyyy-MM-dd)2022-03-15
Days OnlineonlineDaysDays since listing850
Sale PricepriceSale price after Coupon, local currency (e.g., USD)25.99
Couponcoupon>0 = discount amount (500=$5); <0 = percentage (-10=10% off)-15
Platform FeeplatformFeePlatform commission, local currency3.90
FBA FeesfbaFeesFBA fulfillment fee, local currency5.40
FBA DetailfbaDetailFBA breakdown: [delivery fee, month:storage fee, ...][475, "1-9:5", "10-12:15"]
ProfitprofitAmountSale price - FBA - commission, local currency16.69
Profit RateprofitRateProfit margin, e.g., 25.83 = 25.83%25.83
BSR RanksalesRankBest Seller Rank in main category1523
BSR TrendrankTrendMain category rank history, interleaved [date, rank, ...][20250101, 1523, ...]
Sub-BSR TrendbsrRankTrendSub-category rank history per node[{NodeId, Rank: [...]}]
RatingratingCurrent rating (0.0-5.0)4.70
Rating CountratingsNumber of ratings12580
Star DistributionfiveStarRatings / fourStar... / oneStar...Star percentage, e.g., 57.7 = 57.7%57.7
Daily Sales TrendlistingSalesVolumeOfDailyTrendInterleaved [date, volume, ...]; -1 = cannot estimate[20250101, 150, ...]
Monthly Sales TrendlistingSalesVolumeOfMonthTrendInterleaved [date, volume, ...]; -1 = cannot estimate[20250101, 4500, ...]
Daily Revenue TrendlistingSalesOfDailyTrendInterleaved [date, revenue, ...]; unit = cents; -1 = N/A[20250101, 38985, ...]
Monthly Revenue TrendlistingSalesOfMonthTrendInterleaved [date, revenue, ...]; unit = cents; -1 = N/A[20250101, 1169550, ...]
Price TrendpriceTrendSale price history; unit = cents; -1 = no price that day[20250101, 2599, ...]
List Price TrendlistPriceTrendStrikethrough price history; unit = cents; -1 = N/A[20250101, 3999, ...]
Buybox SellerbuyboxSellerBuybox winning seller nameAnkerDirect
Seller CountrybuyboxSellerAddressSeller country code (CN, US, etc.); null if Amazon-operatedCN
FBA StatusisFBAWhether Buybox seller uses FBAtrue
Seller CountsellerNumNumber of sellers on this listing3
A+ ContentaPlusHas A+ contenttrue
VideohasVideoHas video on listingtrue
Brand StorehasBrandStoreHas brand storefronttrue
WeightweightWeight in grams350
SizesizeDimensions in cm [longest, 2nd, shortest][18.5, 8.2, 3.1]

Supported Marketplaces

US (United States), GB (United Kingdom), DE (Germany), FR (France), IN (India), CA (Canada), JP (Japan), ES (Spain), IT (Italy), MX (Mexico), AE (United Arab Emirates), AU (Australia), BR (Brazil), SA (Saudi Arabia)

Default marketplace is US. Use us when the user doesn't specify a marketplace.

Note: Sorftime uses lowercase codes (e.g., us, gb, de), and UK is coded as gb (not uk).

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/sorftime_product_detail.py directly to run queries.

How to Build Queries

The key parameters are asin and marketplace (both required), plus optional trend date range controls.

Principles for Building Queries

1. Always specify the marketplace: Use lowercase site codes, e.g., us, de, jp 2. Choose trend inclusion carefully: Default includes trends (last 15 days). Set includeTrend: 2 if only basic product info is needed — this saves cost and speeds up response 3. Specify date range for historical analysis: Use queryTrendStartDate and queryTrendEndDate (yyyy-MM-dd) when users need trends beyond the default 15 days. Be aware this costs double 4. Batch ASINs when comparing: Up to 10 ASINs can be queried at once, comma-separated — use this for competitive comparison rather than calling one at a time

Query Examples for Common Scenarios

1. Quick product check (default 15-day trend)

asin: B00FLYWNYQ, marketplace: us

2. Long-range trend analysis (specify dates)

asin: B00FLYWNYQ, marketplace: us
queryTrendStartDate: 2025-01-01, queryTrendEndDate: 2025-03-31

3. Batch ASIN comparison

asin: B0088PUEPK,B00U26V4VQ,B0CVM8TXHP, marketplace: us

4. Product info only, no trends

asin: B0088PUEPK, marketplace: us, includeTrend: 2

5. BSR ranking history (German market)

asin: B00FLYWNYQ, marketplace: de
queryTrendStartDate: 2024-06-01, queryTrendEndDate: 2025-01-01

Trend Data Interpretation

Trend arrays use an interleaved format: even indices are dates, odd indices are values.

[20250101, 150, 20250102, 180, 20250103, 165, ...]
 ^date     ^val ^date     ^val ^date     ^val
  • Sales volume/revenue trends: value of -1 means "cannot estimate" (e.g., category changed to Amazon Renewed)
  • Price trends: units are in local currency smallest unit (cents for USD); -1 means no available price that day
  • BSR rank trends: for bsrRankTrend, format is [{NodeId: xxx, Rank: [date, rank, ...]}] per sub-category

Display Rules

1. Present data only: Show query results in clear tables without subjective business advice 2. Ranking clarification: When showing ranking data, remind users that lower values mean better rankings 3. Price unit awareness: Trend data uses smallest currency unit (cents for USD). Convert to standard currency when displaying to users 4. Sales estimation caveat: Values of -1 in sales/revenue fields mean "cannot estimate" — explain this to the user rather than showing -1 directly 5. Trend visualization: When showing trend data, present key data points in a readable table rather than dumping raw arrays 6. Error handling: When a query fails, explain the reason based on the msg field and suggest adjusting query criteria

Important Limitations

  • Max 10 ASINs per query
  • Trend cost: Default returns last 15 days; querying > 15 days costs double
  • Non-structured data: Results do not support secondary analysis via _dataQuery_executeDynamicQuery
  • Sales estimation: Products in non-standard categories (e.g., Amazon Renewed) may return -1 for sales fields

User Expression & Scenario Quick Reference

Applicable - Product detail and trend queries by ASIN:

User SaysScenario
"查一下这个ASIN的销量走势"Sales trend
"这个产品最近价格变化如何"Price history
"帮我看看这个产品的利润空间"Profit analysis
"这个ASIN的BSR排名趋势"Ranking history
"对比一下这几个ASIN的数据"Multi-ASIN comparison
"这个产品的FBA费用是多少"FBA fee breakdown
"产品上架多久了,评分怎么样"Basic product info

Not applicable - Needs beyond single-product detail:

  • Searching/filtering products across a category or brand (use Sorftime Product Search)
  • ABA search term ranking data (use ABA Data Explorer)
  • Advertising / PPC strategy
  • Product reviews content analysis
  • Patent or trademark checks

Boundary judgment: When users say "product analysis" or "competitor comparison", if it boils down to checking specific ASINs' detail data and trend curves, then this skill applies. If they're asking to discover or filter products across a market, 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/sorftime_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, visit [LinkFox Skills](https://skill.linkfox.com/).

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