---
name: alibaba-monthly-store-report
description: Retrieve Alibaba International Station (Alibaba.com) store's operational data and P4P ads metrics, analyze month-over-month (MoM) changes, perform industry benchmarking (本店 vs 行业平均 vs 行业 TOP 10%), make status diagnoses (✅ 领先, 🟡 观察, 🔴 预警), and export a professional Excel "体检表" (Sheet 1) along with a structured Markdown/Word diagnostic report. Trigger this skill when the user requests a monthly store business report, shop physical check-up (店铺体检), Alibaba.com backend data collation, or provides "月报告整理" instructions.
---

# Alibaba International Station Monthly Business Report & Diagnosis

This skill automates the extraction and compilation of an Alibaba International Station (Alibaba.com) store's monthly business performance data. It creates a standardized Excel workbook containing a "店铺体检表" (Sheet1) comparing last month ("上月"), the month before last ("上上月"), month-over-month percentage changes ("月环比"), industry averages ("行业均值"), and industry top 10% averages ("行业 TOP 10%"). It also produces an operational diagnosis report highlighting status diagnoses, peer gaps, and top 3 priority operational issues.

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## 🎯 Scope of Indicators
The physical check-up table tracks 19 core metrics across four dimensions:
1. **Traffic & Acquisition**:
   - 搜索曝光次数 (Search Impressions)
   - 搜索点击次数 (Search Clicks)
   - 点击率CTR（搜索） (Search Click-Through Rate)
   - 店铺访问PV (Store Pageviews PV)
2. **Leads & Conversion**:
   - 商机量（询盘+TM） (Leads: Inquiries + TM chat)
   - 商机转化率（访客→商机） (Visitor-to-Lead Conversion Rate)
   - 商机→订单转化率 (Lead-to-Order Conversion Rate)
3. **Service & Logistics**:
   - 平均回复时长（30天） (Average Response Time - last 30 days)
   - 5分钟快速回复率 (5-Min Fast Response Rate)
4. **Transaction & Product**:
   - 成交订单数 (Completed Orders)
   - 信保实收金额 (Credit Insurance Amount Received)
   - 在架产品总数 (Active Products Online)
   - 90天新发品数 (New Products Published in 90 Days)
   - 实力优品数 (High-Quality Products)
   - 爆品数 (Star/Explosive Products)
5. **P4P Marketing (Ads)**:
   - 直通车花费 (P4P Ads Spend)
   - 直通车商机量 (P4P Leads)
   - 直通车商机转化率 (P4P Lead Conversion Rate)
   - 直通车商机成本(CPL) (Cost Per Lead)

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## 🕰️ Time Horizon Logic

* **Time context**: Dynamically calculated based on the run date.
* **"上月" (Last Month)**: The calendar month immediately preceding the current month.
  * *Example*: If run on `2026-06-09`, "上月" is **May 2026** (May 1 - May 31).
* **"上上月" (Month Before Last)**: Two calendar months preceding the current month.
  * *Example*: If run on `2026-06-09`, "上上月" is **April 2026** (April 1 - April 30).
* **"月环比" (MoM Change)**: Compares "上月" against "上上月" using the formula:
  $$\text{月环比} = \frac{\text{上月} - \text{上上月}}{\text{上上月}} \times 100\%$$

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## 🌐 Data Sources & Extraction Selectors

### Source A: Core Business Performance Summary
* **URL**: `https://data.alibaba.com/newsummary`
* **Interaction Workflow**:
  1. Access the URL with an active login session.
  2. Locate the date selector element (often matching `class="next-radio-label"` or standard date input).
  3. Click "自定义" (Custom) or select "上个月" (Last Month) to extract metrics, recording them into the "上月数据" column.
  4. Repeat for "上上个月" (Month before last), recording them in the "上上月数据" column.

### Source B: P4P Ads Performance
* **URL**: `https://i.alibaba.com/ads/p4p/report.htm`
* **Interaction Workflow**:
  1. Navigate to the ads report center.
  2. Set the custom date filter for "上月" (e.g. May 1 - May 31).
  3. Extract "直通车花费", "直通车商机量", and "直通车商机成本(CPL)".
  4. Set the custom date filter for "上上月" (e.g. April 1 - April 30) and repeat extraction.

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## 📋 Status Diagnosis & Benchmarking Rules

Every metric is annotated with a **"状态诊断" (Status Diagnosis)**:
- 🟢 **✅ 领先 (Leading)**: Store performance is greater than or equal to the "行业 TOP 10%" average.
- 🟡 **🟡 观察 (Observing)**: Store performance is between the "行业均值" and "行业 TOP 10%".
- 🔴 **🔴 预警 (Alert)**: Store performance is below the "行业均值".

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## 📊 Deliverable Structure

### Output 1: Excel Store Physical Check-Up Workbook
* **Filename**: `Alibaba_Store_Physical_Checkup_[Month].xlsx`
* **Sheet 1 Name**: `体检表`
* **Format**: Standard table layout including columns for indicator names, current data, comparison data, percentage MoM change, industry average, top 10% benchmark, and status icon.

### Output 2: Structured Operation Diagnosis Report
* **Filename**: `Alibaba_Operation_Diagnosis_Report_[Month].md`
* **Sections**:
  1. **Executive Summary & Background**: (Run time, period scopes).
  2. **Dimension-Level Analysis** (Traffic, Leads, Service, Products):
     - **现状 (Current Status)**: Clear summary of actual metrics.
     - **同行差距 (Peer Gap)**: Deep comparison against industry averages and top 10%.
     - **问题根源 (Root Causes)**: Pinpoint why certain metrics are in red/yellow status.
  3. **Operational Core Issues**: Highlight the **top 3 priority operational bottlenecks** that need immediate resolution.
  4. **Actionable Operations Roadmap**: Clear, timeline-based corrective steps.

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## 🛠️ Automated Execution Workflow (For Agents)

When a request for monthly shop analysis is made:
1. Ensure the user is logged into Alibaba.com. If not authenticated, prompt them to run browser sessions or login.
2. If real-time crawlers or browser sessions are available, use them to navigate to `data.alibaba.com` and auto-scrape the required indices.
3. If crawler access fails or some metrics are missing, prompt the user for manual entry of the missing parameters but **never fabricate data**.
4. Execute `scripts/generate_report.py` with the collected raw metrics to compile the Excel sheet and Markdown audit.
5. Present clickable relative markdown links to both the Excel file and Markdown report.
