linkfox-mpstats-ozon-product-trend

name: linkfox-mpstats-ozon-product-trend description: MPSTATS Ozon 俄罗斯站单个 SKU 的分日时间序列表现。按日期粒度返回一个 Ozon 商品的销量、价格、库存、评分等指标，可选附带搜索位次/可见性数据，用于验证增长趋势、季节性、异常波动。当用户提到 Ozon 趋势、Ozon 销量趋势、Ozon 价格走势、Ozon 分日数据、Ozon 库存走势、Ozon 搜索位次、Ozon 商品历史、MPSTATS trend, Ozon daily performance, Ozon time series, Ozon search visibility, Russian marketplace product history 时触发此技能。即使用户未明确说"MPSTATS"，只要意图是看某个 Ozon 商品的分日/时间段走势，也应触发此技能。

MPSTATS Ozon Product Trend (Daily Time-Series)

This skill returns a daily time-series of a single Ozon (Russia) SKU — sales units, price, stock, rating, and optionally search-position / visibility metrics. It is the go-to for validating growth, seasonality, or anomalies for a specific product.

Core Concepts

Single-SKU scope: Each call analyzes exactly one productId. For batch per-SKU snapshots (period aggregates), use mpstats-ozon-product-detail instead.

Daily granularity: The response is an array of daily points (top-level field data) across the [startDate, endDate] window. Each point carries a hasData boolean — if hasData=false, the day has no observation (distinct from sales=0 with hasData=true).

T-1 delay: MPSTATS trend data is delayed by one day; the latest selectable end date is yesterday. Today or future dates are rejected.

Search-visibility add-on: Set includeSearchStats: true to append search-position / visibility signals. Some niches (especially small categories) may not have search-stats coverage — expect partial or empty fields in those cases.

Parameters

API Usage

This tool calls the LinkFox tool gateway API. See references/api.md for calling conventions, request parameters, response structure, and error codes. You can also execute scripts/mpstats_ozon_product_trend.py directly for ad-hoc queries.

Usage Examples

1. Monthly trend for a SKU

{
  "productId": 1786874757,
  "startDate": "2025-03-01",
  "endDate": "2025-03-31"
}

2. Trend with search visibility

{
  "productId": 1786874757,
  "startDate": "2025-02-01",
  "endDate": "2025-02-28",
  "includeSearchStats": true
}

3. Combined FBO+FBS trend

{
  "productId": 151623766,
  "startDate": "2025-01-01",
  "endDate": "2025-01-31",
  "includeFbs": true
}

How to Chain with Other Ozon Skills

1. Discovery → trend: Use mpstats-ozon-product-search to find a SKU, then check growth / volatility here before committing.
2. Aggregate vs time-series: mpstats-ozon-product-detail gives a one-number-per-metric period view; this skill shows the day-by-day shape behind those numbers.
3. Drill-down → trend: After brand-products / category-products / seller-products surfaces a hot SKU, use this skill to validate whether the hotness is recent, seasonal, or sustained.

Display Rules

1. Prefer a simple table or sparkline-friendly output — one row per date with date, price, sales, balance, rating, comments; do not overfit a 90-point series into a single paragraph.
2. Use hasData to distinguish gaps from zero sales — hasData=false means the day has no observation; don't report it as a zero-sale day.
3. Call out anomalies — large single-day spikes or stockouts (balance=0 runs where hasData=true) should be flagged factually, not as buying advice.
4. Currency is RUB unless upstream layer is already converting (the currency field per point carries the symbol, e.g. ₽); state the currency when showing price movement.
5. Revenue is not returned per day — if the user asks for daily revenue, estimate via sales * price and note it's an estimate.
6. includeSearchStats gaps — when no search-visibility fields come back, note "搜索位次数据在该赛道暂不可用" rather than silently omitting.
7. No business advice — present the shape; leave "should we buy this listing?" to the user.

Important Limitations

• Single SKU per call — cannot pass a list of productIds; loop at the Agent layer if needed.
• T-1 data — endDate cannot be today or a future date.
• Search stats optional — includeSearchStats=true doesn't guarantee coverage for all niches.
• Ozon-only — Wildberries and other Russian marketplaces are not covered.
• Missing days — the series may have nulls / gaps where no data was captured; do not treat nulls as zero sales.

User Expression & Scenario Quick Reference

Applicable — Single-SKU temporal analysis:

Not applicable — Needs beyond single-SKU time-series:

• Batch snapshot of many SKUs → mpstats-ozon-product-detail
• Brand / category / seller drill-down → matching *-products skill
• Pre-IDed discovery → mpstats-ozon-product-search

Boundary judgment: Use this skill when the question starts with "how did this ONE product change over time". For multi-SKU comparisons or dimension-level filtering, go elsewhere.

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/mpstats_ozon_product_trend.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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