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Bundle Margin Calculator

核心功能:通过交互明确需求,基于输入的商业假设、价格成本、流量转化等数据,生成Python脚本建模对比基准与套餐场景,展示敏感性分析并提供决策建议。典型场景:电商团队推出套餐、折扣前的经济验证,快速压力测试商品决策。输出含评估结果、风险点、建议及可复用脚本,辅助判断方案可行性。

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热度 81.03
最近更新 2026-05-11 17:32:53
分类:开发依赖:代码执行 / 内部调用大模型应用场景:代码生成 / 数据处理 / 财务 / 市场分析

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add bundle-margin-calculator
Metadata
name
bundle-margin-calculator
description
Calculate whether a product bundle improves average order value without quietly destroying contribution margin. Use when teams want to test bundle structures with clearer economics.

Bundle Margin Calculator

Compare bundle upside against margin reality so the offer is not just bigger, but smarter.

先交互,再计算

开始时先问:

  1. bundle 结构是什么?
    • 固定套装
    • 买 A 加价购 B
    • 多件折扣
  2. 你们想看的是 AOV 提升,还是 contribution margin 改善?
  3. 是否要考虑 attach rate、运费变化、赠品成本、潜在自我蚕食?
  4. 你们有没有现有 bundle 评估逻辑?
  5. 要沿用现有逻辑,还是让我给推荐框架?

Python script guidance

有结构化数据时:

  • 生成 Python 脚本建模 bundle 方案
  • 输出 baseline vs bundle scenario
  • 展示 attach rate / margin sensitivity
  • 返回可复用脚本

Solves

Many ecommerce teams make pricing or offer decisions with incomplete economics:

  • they see revenue upside but not margin drag;
  • they model one variable but ignore knock-on effects;
  • they test offers without clear guardrails;
  • they scale offers before checking break-even logic.

Goal: Turn offer assumptions into a clearer economic view that is easier to evaluate and act on.

Use when

  • You want to compare offer scenarios before launching
  • A discount, bundle, or upsell idea sounds good but needs economic validation
  • Growth teams need a faster way to pressure-test merchandising decisions
  • Teams want clearer go / watch / no-go logic before scale

Inputs

  • Core commercial assumptions relevant to the scenario
  • Price and cost structure
  • Margin or refund assumptions
  • Traffic / conversion or attach-rate assumptions
  • Constraints or guardrails

Workflow

  1. Clarify the baseline commercial setup and bundle logic.
  2. Model the scenario inputs that change order economics.
  3. Surface upside, downside, and sensitivity.
  4. Identify the biggest weak points or break-even pressure.
  5. Recommend whether to test, revise, or avoid the scenario.
  6. Return reusable Python script.

Output

  1. Baseline view
  2. Scenario result
  3. Margin / break-even implications
  4. Key risks and weak points
  5. Recommendation
  6. Python script

Quality bar

  • Output should be commercially interpretable, not just a raw formula dump.
  • Recommendations should stay grounded in ecommerce economics.
  • Weak points should be clearly separated from upside assumptions.
  • The result should help a team decide what to test next.

Resource

See references/output-template.md.