Algo Price Van Westendorp logo

Algo Price Van Westendorp

Organization
asgard-ai-platform
algo-price-van-westendorp

Conduct Van Westendorp Price Sensitivity Meter analysis to identify acceptable price ranges. Use this skill when the user needs to determine price boundaries for a new product, find the optimal and indifference price points, or survey-based pricing research — even if they say 'what should we charge', 'price sensitivity survey', or 'acceptable price range'.

Overview

Publisherasgard-ai-platform
Repositoryskills
Skill namealgo-price-van-westendorp
Stars
236
Forks
29
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by asgard-ai-platform on GitHub. Read the source before you install it.

Installation

Install the Algo Price Van Westendorp AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/asgard-ai-platform/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/algo-price-van-westendorp .claude/skills/algo-price-van-westendorp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Algo Price Van Westendorp in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Algo Price Van Westendorp on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Algo Price Van Westendorp is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)
  4. 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

json
{
  "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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Algo Price Van Westendorp AI skill do?

Conduct Van Westendorp Price Sensitivity Meter analysis to identify acceptable price ranges. Use this skill when the user needs to determine price boundaries for a new product, find the optimal and indifference price points, or survey-based pricing research — even if they say 'what should we charge', 'price sensitivity survey', or 'acceptable price range'.

Why use Algo Price Van Westendorp on TypingMind?

Because you install it once and use it with any model. Algo Price Van Westendorp is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Algo Price Van Westendorp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/asgard-ai-platform/skills/tree/main/algo-price-van-westendorp. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Algo Price Van Westendorp?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Algo Price Van Westendorp?

As many as you like. As long as a model supports skills, you can use Algo Price Van Westendorp with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Algo Price Van Westendorp AI skill free?

Yes. It is published on GitHub by asgard-ai-platform under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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