Seerfar-Ozon商品详情

Seerfar Ozon 商品详情查询：按 Ozon 商品 SKU 拉取单个商品的完整详情，返回标题、价格（卢布）、评分、评论数、QA数、统计窗口内总销量与日均销量、销售额、库存、类目排名、每日销量趋势、品牌、卖家、配送方式（FBO/FBS/OZON）、重量、上架时间/天数/月数等。用于单品深度分析、竞品商品拆解、Ozon 选品评估、Listing 诊断、销量趋势与类目排名跟踪。当用户提到 Ozon 商品详情、Ozon 单品分析、Ozon SKU 查询、竞品商品数据、Ozon 销量趋势、Ozon 类目排名、Ozon 库存、Ozon 上架时间、Seerfar Ozon 商品搜索、Ozon product detail, Ozon SKU lookup, single product analysis, competitor product teardown, Ozon sales trend, category rank 时触发此技能。即使用户未明确提到"Seerfar"，只要其意图是查看某个 Ozon 商品的详细数据，也应触发此技能。

Seerfar Ozon Product Detail Search

This skill fetches the full detail of a single Ozon product by its SKU from the Seerfar analytics database — title, price (₽), rating, reviews, QA count, sales (total + daily average + daily trend), revenue, stock, category rank, brand, seller, fulfillment (FBO/FBS/OZON), weight and listing age. The starting point for single-product deep analysis, competitor product teardown, listing diagnostics and sales-trend tracking.

Core Concepts

Unit of data is a single product, looked up by sku: pass one Ozon SKU, get that product's full detail. This is a product-level view (one SKU), not a shop catalog, keyword or category view.

Where the SKU comes from: sku is the Ozon product SKU — the same sku returned by other Seerfar Ozon tools (shop search, keyword back search, category search, market keyword search). If the user only has a product name, URL or shop, first obtain the sku from one of those listing-level sources, then call this skill for the deep dive.

Sales window: dateRange controls the sales/metrics window — totalSales, dailySales, totalRevenue and salesTrendVOList are computed over this range. Default past_30_days. Options: past_7_days / past_30_days / past_60_days / past_90_days / past_180_days / past_365_days.

Sales & price currency: price is in Russian rubles (₽), indicated by currency. totalSales is units over the window; dailySales is the average units/day; totalRevenue is revenue over the window.

Listing age: upTime is the listing timestamp (ms); upDays / upMonths are the derived age in days / months.

Parameters

Parameter	Type	Required	Description
sku	string	yes	Ozon product SKU (e.g. 175924376). The same sku from other Seerfar Ozon tools.
dateRange	string	no	Sales/metrics window. Default past_30_days. One of: past_7_days, past_30_days, past_60_days, past_90_days, past_180_days, past_365_days.
uId	string	no	User ID.
memberId	string	no	Member ID (data attribution).

Only sku is required.

调用方式

• API 端点：POST /seerfar/ozon/productDetailSearch（完整参数/响应/错误码见 references/api.md）
• Python 脚本：python scripts/seerfar_ozon_product_detail_search.py '<JSON 参数>' [--inline]
• 成本约束：本工具会消耗积分；同一会话同一参数组合默认只调用一次，脚本带 24h 本地缓存。失败/空结果不得自动换关键词、翻页或改邮编连续试探；需要继续检索时先向用户说明会产生额外消耗。

输出策略（脚本默认行为）：

• 始终将完整响应写入 <cwd>/linkfox/<YYYY-MM-DD>/<session>/data/linkfox-seerfar-ozon-product-detail-search-<timestamp>.json（<cwd> 为脚本执行时的工作目录，在 Claude Code 里即当前项目目录；<session> 取自环境变量 SESSION_ID，按用户任务自动聚合；禁止写入 /tmp，当前目录不可写则报错）
• 响应体 ≤ 8 KB：落盘后把完整 JSON 打印到 stdout
• 响应体 > 8 KB：落盘后 stdout 只输出摘要（顶层字段、常见计数如 total/costToken、最大列表字段的长度 + 前 3 条样本）
• 加 --inline 强制全量打印到 stdout（同样落盘）

读数据建议：先看摘要判断是否足够；需要具体字段时优先用 jq或ConvertFrom-Json 从保存的 json 文件按需抽取，避免整份 JSON 进入上下文。

Usage Examples

1. Default 30-day detail for a product

json

{"sku": "175924376"}

2. Last 7 days (recent momentum)

json

{"sku": "175924376", "dateRange": "past_7_days"}

3. Last 90 days (quarterly trend)

json

{"sku": "175924376", "dateRange": "past_90_days"}

4. Full year (lifecycle view)

json

{"sku": "175924376", "dateRange": "past_365_days"}

How to Build Queries

1. Resolve the SKU first: if the user gives a product name, URL or shop rather than a SKU, obtain the sku from a listing-level Seerfar Ozon source (shop search / keyword back search / category search / market keyword search) before calling this skill.
2. Pick dateRange by intent: short windows (past_7_days / past_30_days) for recent momentum and current stock; long windows (past_90_days / past_180_days / past_365_days) for lifecycle, seasonality and ranking stability.
3. Read aggregates, then trend: start with top-level totalSales / dailySales / totalRevenue / stock / categoryRanks for a snapshot, then drill into salesTrendVOList for the daily series.
4. One SKU per call: this endpoint takes a single sku; to compare products, call once per SKU.

Display Rules

1. Present data only: show the product metrics in a clear layout without subjective advice.
2. Lead with identity + snapshot: title, sku, price (₽), reviewRating (reviewCount reviews, questionsAndAnswers Q&A), then the sales snapshot (totalSales, dailySales, totalRevenue, stock) and the window (startDate–endDate).
3. Category ranks: categoryRanks is a monthly rank history ({date, rank, count}) — it has no category name, so show the category path from categoryInfo (titlePath / cnTitlePath) alongside the rank history.
4. Sales trend: salesTrendVOList is a daily series ({date, sales, revenue, price, stock, reviewCount, reviewRating}) — summarize (peak day, trend direction) rather than dumping every row; offer the persisted file for the full series. Some days may have sales: 0 — treat as no-sales, not missing data.
5. Seller & brand: show sellerName (sellerId — negative means an Ozon platform seller) and brandName (brandId) so the user can trace the seller/brand.
6. Fulfillment: fulfillment is an array (e.g. ["FBO"], or ["OZON"] for platform-sold items); join multiple values with /.
7. Listing age: render upTime as a date (ms timestamp) alongside upDays / upMonths.
8. Conditional fields: weight (physical goods only) and grossMargin are schema-defined but absent for some products (e.g. digital goods / Ozon platform sellers) — show - when missing rather than failing. monthlySalesUnits / monthlySalesRevenue mirror the window's totalSales / totalRevenue and are safe to read directly.
9. Empty result: a non-existent sku returns success with total:0 and empty products — tell the user the SKU may be wrong rather than reporting a system error.
10. Error handling: when code is not "200" (or errcode is not 200), explain from msg / errmsg and suggest fixes (check SKU, retry on rate-limit).

Important Limitations

• sku is required; omitting it returns a parameter error.
• Single-SKU endpoint: returns one product's detail; no batch/list mode. Compare products by calling once per SKU.
• dateRange only affects sales aggregates + trend: product metadata (title, price, rating, brand, seller, weight, fulfillment) is a point-in-time snapshot, not windowed.
• Conditional fields: weight (physical goods only) and grossMargin are schema-defined but not always returned — absent for digital goods / Ozon platform sellers. monthlySalesUnits / monthlySalesRevenue are returned and mirror the window's totalSales / totalRevenue.
• Sales/revenue are Seerfar model estimates over the chosen window, not Ozon-official figures.
• total reflects returned record count (1 when the SKU is found), not a catalog total.

User Expression & Scenario Quick Reference

Applicable — deep-dive on one Ozon product:

User Says	Scenario
"查看这个 Ozon 商品的详情" / "这个 SKU 的数据"	Single product detail
"这个竞品最近30天卖了多少" / "日均销量多少"	Sales snapshot (totalSales / dailySales)
"这个商品销售额多少"	Revenue (totalRevenue)
"这个商品库存多少"	Stock check (stock)
"这个商品在类目里排第几"	Category rank (categoryRanks)
"这个商品最近销量趋势" / "哪天卖得最好"	Daily sales trend (salesTrendVOList)
"这个商品是谁家的/什么品牌"	Seller + brand (sellerName / brandName)
"这个商品上架多久了"	Listing age (upDays / upMonths)

Not applicable — needs beyond one product's detail:

• A shop's full product catalog → use the Seerfar Ozon shop search skill.
• Discovering Ozon keywords → use market keyword search / keyword mining / keyword back search.
• Browsing the category tree → use category search.
• Multiple products' summary at once → call this skill per SKU, or use a listing-level source.

Boundary judgment: if the user already has a specific Ozon SKU (or obtained one from a listing-level source) and wants that product's full metrics — sales, revenue, stock, category rank, trend, brand, seller — start here. If they want to discover products, keywords or shops, route to the corresponding Seerfar Ozon skill first.

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.

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