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algo-price-van-westendorp韦斯滕多普的算法价格

Agent Skill

algo-price-van-westendorp 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

349

周安装

15

GitHub Stars

124

下载量

122
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:algo-price-van-westendorp(韦斯滕多普的算法价格)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/algo-price-van-westendorp
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill algo-price-van-westendorp
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/asgard-ai-platform/skills --skill algo-price-van-westendorp

简介

algo-price-van-westendorp 基于消费者感知调研确定产品可接受价格区间。

  • 适合新品定价、服务初始价格设定及快速市场调研场景。
  • 通过四个价格问题收集数据,输出最优价格点和可接受范围。
  • 安装命令:npx skills add https://github.com/asgard-ai-platform/skills --skill algo-price-van-westendorp
  • 需确保有足够样本量(建议100人以上)且数据来源真实可靠

SKILL.md

Van Westendorp Price Sensitivity Meter

Overview

Van Westendorp PSM uses four price perception questions to identify an acceptable price range through intersection analysis. Produces: Point of Marginal Cheapness (PMC), Point of Marginal Expensiveness (PME), Indifference Price Point (IPP), and Optimal Price Point (OPP). Requires survey data from 100+ respondents.

When to Use

Trigger conditions:

  • Setting initial price for a new product or service
  • Identifying the acceptable price range from consumer perception
  • Quick pricing research without complex experimental design

When NOT to use:

  • When you need to measure attribute trade-offs (use conjoint analysis)
  • When you need demand curve estimation (use price elasticity)

Algorithm

IRON LAW: Van Westendorp Identifies an ACCEPTABLE Range, Not Optimal Price
It doesn't account for competition, costs, or willingness to pay at
scale. It tells you WHERE prices are perceived as reasonable, not
what maximizes revenue. Use as input to pricing strategy, not as the
final answer.

Phase 1: Input Validation

Survey 100+ target customers with four questions at various price points:

  1. Too cheap (quality suspect)? 2. A bargain (great deal)? 3. Getting expensive (but would consider)? 4. Too expensive (would not buy)? Gate: 100+ responses, all four curves plottable.

Phase 2: Core Algorithm

  1. For each price point, compute cumulative percentages for each question
  2. Plot four curves: "too cheap" (descending), "cheap/bargain" (descending), "expensive" (ascending), "too expensive" (ascending)
  3. Find intersections:

- OPP = intersection of "too cheap" and "too expensive" (optimal price point) - IPP = intersection of "cheap" and "expensive" (indifference price point) - PMC = intersection of "too cheap" and "expensive" (marginal cheapness) - PME = intersection of "cheap" and "too expensive" (marginal expensiveness)

  1. Acceptable range = [PMC, PME]

Phase 3: Verification

Check: PMC < OPP < IPP < PME (expected ordering). All intersections exist within surveyed range. Gate: Four-point ordering is logical, range is commercially viable.

Phase 4: Output

Return price points and acceptable range.

Output Format

{
  "price_points": {"opp": 299, "ipp": 349, "pmc": 199, "pme": 449},
  "acceptable_range": {"min": 199, "max": 449},
  "metadata": {"respondents": 250, "currency": "TWD", "product": "..."}
}

Examples

Sample I/O

Input: 200 survey responses for a SaaS product, price range tested: $5-$50/month Expected: PMC=$12, OPP=$18, IPP=$22, PME=$35. Acceptable range: $12-$35.

Edge Cases

InputExpectedWhy
Curves don't intersectExtend surveyed rangePrice points tested were too narrow
IPP < OPPUnusual but possibleCheck data quality, may indicate confused respondents
Very wide rangeLow price sensitivityProduct category has high tolerance

Gotchas

  • Hypothetical bias: People say they'd pay more than they actually would. Van Westendorp systematically overestimates willingness to pay.
  • No competitive context: Respondents answer in isolation. Real purchase decisions consider alternatives. Supplement with competitive analysis.
  • Sample representativeness: Results are only valid for the surveyed population. B2B vs B2C, early adopters vs mainstream — all give different ranges.
  • Newton-Miller-Smith extension: Add purchase intent questions at OPP and IPP for more actionable revenue estimates. Standard Van Westendorp alone lacks this.
  • Product must be understood: Respondents need to understand what they're pricing. For novel products, include a clear concept description.

References

  • For Newton-Miller-Smith purchase intent extension, see references/nms-extension.md
  • For survey design best practices, see references/survey-design.md

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.32%
按下载量换算46

Claude

30.05%
按下载量换算37

Cursor

19.85%
按下载量换算24

Gemini CLI

9.29%
按下载量换算11

安全审计

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通过

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Snyk

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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