retail-store-poscore-baseline-analysis

门店历史基线分析工具。基于Agent API数据库视图,提供多周期基线+四分位分析。 核心能力: 1. 多周期基线(13周/季度、26周/半年、52周/全年、12个月) 2. 多维度分组(按星期几分组、自然周、自然月) 3. 四分位分析(P25/P50/P75,识别异常区间) 4. 基线类型(weekday按星期几分组、week自然周、month自然月) 5. 数据质量检查(最小样本数要求) 数据源:v_gmv_daily_by_store、v_gmv_weekly_by_store、v_gmv_monthly_by_store 触发条件: - 用户需要基线对比(如"和历史比怎么样") - 用户分析周期规律(如"周一通常卖多少") - 用户识别异常(如"今天的业绩正常吗")

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Install skill "retail-store-poscore-baseline-analysis" with this command: npx skills add gwyang7/retail-store-poscore-baseline-analysis

历史基线分析 Skill

技能名称

store-poscore-baseline-analysis

版本

v1.0

功能描述

基于Agent API(数据库视图),提供多周期基线+四分位分析。

数据源

  • v_gmv_daily_by_store(日粒度)
  • v_gmv_weekly_by_store(周粒度)
  • v_gmv_monthly_by_store(月粒度)

核心能力

1. 基线类型

  • WEEKDAY (weekday) - 按星期几分组(周一、周二...)
  • WEEK (week) - 自然周(周一至周日)
  • MONTH (month) - 自然月(每月1日到最后1日)

2. 基线周期

  • P13W (13w) - 13周(季度)
  • P26W (26w) - 26周(半年)
  • P52W (52w) - 52周(全年)
  • P12M (12m) - 12个月

3. 四分位分析

  • P25(第25百分位)
  • P50(中位数)
  • P75(第75百分位)
  • 识别异常区间

4. 最小样本要求

  • weekday: 至少6个样本
  • week: 至少6周
  • month: 至少6个月

使用示例

from analyze import analyze_baseline, BaselineType, BaselinePeriod

# 分析门店历史基线
result = analyze_baseline(
    store_id="416759_1714379448487",
    baseline_type=BaselineType.WEEKDAY,
    baseline_period=BaselinePeriod.P13W,
    end_date="2026-03-25"
)

版本

v1.0.0 - 多周期基线、四分位分析

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