---

name: linkfox-sorftime-product-detail

description: "基于Sorftime数据按ASIN查询亚马逊产品详情与历史趋势，涵盖14个站点。当用户提到Sorftime产品详情、ASIN详情查询、销量走势、价格曲线、价格历史、BSR排名历史、BSR趋势、利润分析、FBA费用分析、毛利率、产品趋势分析、日销量月销量、销售额趋势、Deal促销历史、product detail, sales trend, price history, BSR ranking, profit analysis, FBA fees时触发此技能。即使用户未明确提及\"Sorftime\"，只要其需求涉及按ASIN查询亚马逊产品详情或历史趋势数据，也应触发此技能。"

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

Response data covers the following categories (see references/api.md for complete field reference):

• Basic info: title, brand, ASIN, listing URL, images (main + A+), store name, bullet points, product badges, off-sale status, last update date, weight, size
• Variations: parent ASIN, variation count, child ASINs, variation attributes
• Pricing & profit: sale price, coupon, platform fee, FBA fees (with detail breakdown), FBM shipping cost, profit amount & rate
• Sales: official monthly sales (Amazon-published)
• Rankings: BSR rank, category tree, sub-category rankings, listing date, days online
• Ratings: rating score, rating count, star distribution (1-5 star percentages)
• Seller: Buybox seller name/ID/country, FBA status, seller count
• Listing features: A+ content, video, brand store, feature ratings, product info, properties
• Promotions: brand promotion, deal type, extra savings
• Trends (time-series): BSR rank, sub-BSR rank, daily/monthly sales volume, daily/monthly revenue, price, list price, deal status

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
• Deal trend: value 1 = has active Deal that day, 0 = no Deal

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. Off-sale handling: When offSale is true, clearly inform the user the product is currently unavailable/off-sale
7. 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:

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.

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.

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