Pricing And Wtp logo

Pricing And Wtp

Community
MaxKmet
pricing-and-wtp

Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and market_insights monetization signals. Recommends pricing model, freemium conversion estimate, and annual/monthly strategy.

Overview

PublisherMaxKmet
Repositoryidea-validation-agents
Skill namepricing-and-wtp
Stars
488
Forks
56
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by MaxKmet on GitHub. Read the source before you install it.

Installation

Install the Pricing And Wtp 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.
    https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp
  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/MaxKmet/idea-validation-agents.git /tmp/idea-validation-agents
mkdir -p .claude/skills
cp -r /tmp/idea-validation-agents/skills/pricing-and-wtp .claude/skills/pricing-and-wtp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pricing And Wtp 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 Pricing And Wtp 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 Pricing And Wtp 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.

Skill: pricing-and-wtp

Purpose

Determine what users would actually pay, not what the developer hopes to charge. Anchor WTP to pain intensity, desire strength, competitive pricing, and real monetization signals from market_insights — never to the cost of building. Pricing is the single decision with the highest leverage on unit economics: a $1/mo difference at 1,000 users is $12K/year.

Input

  • Idea slug
  • memory/ideas/<slug>/idea.md (app concept, key features)
  • memory/ideas/<slug>/user_extraction.json (pain intensity)
  • memory/ideas/<slug>/competitors.json (competitive pricing, pricing models in use)
  • memory/ideas/<slug>/desire_scores.json (desire strength, primary/secondary drivers)
  • memory/market_insights/<niche>-*-<YYYY>-<MM>.md (trend data — use all available platform files)

Using Market Insights

Trend analysis files provide monetization reality-checks that desk research alone cannot:

FieldHow it informs pricing
monetization_evidenceThe strongest signal. If competitors are visibly making money (in-app purchases mentioned in reviews, subscription pricing in App Store listings, sponsored content), the market has validated WTP. If monetization_evidence is empty across all platforms, WTP is unproven — discount aspirational price and flag risk.
Platform narratives — RedditUsers discuss pricing directly: "I'd pay for X if...", "Y is overpriced because...", "$Z/mo is too much for just..." These are raw WTP signals. Extract any price mentions.
Platform narratives — App StoreReview complaints about pricing reveal the ceiling: "Great app but not worth $X/mo." Review praise about pricing reveals the floor: "Amazing value at $X."
Platform narratives — TikTokCreator recommendations often mention price as part of the hook ("this FREE app...", "worth every penny of $X"). Signals whether freemium or premium positioning resonates with the audience.
trend_velocityRising-fast markets can support premium pricing (early adopters pay more). Declining markets face price pressure (users comparison-shop harder).

Pricing Models

ModelBest forRiskIndie sweet spot
Freemium → subscriptionHabit-forming apps with daily use, feature-gated valueConversion friction; most users stay free forever$3–$8/mo or $20–$50/yr
Subscription only (paywall)High-value, immediately obvious ROI, professional toolsHigh churn risk; must prove value fast$5–$15/mo
One-time purchaseUtilities, tools with clear one-time value, privacy-focused appsNo recurring revenue; must rely on new users$3–$10
Consumables / creditsVariable usage (AI generations, exports, premium content)Unpredictable revenue; usage may decline$1–$5 per credit pack
Freemium + consumablesApps where core is free but power usage costs (AI, storage)Complex to balance free vs. paid tiersFree tier + $2–$10 packs
Lifetime dealLaunch phase only — use for early adopter cash and reviewsDestroys LTV; never use as primary model$20–$60 (3–5× annual price)
Tip jar / patronageContent-focused, community-driven, or open-source-adjacentVery low conversion; unreliable revenue$1–$5 tips, rare

Model selection criteria

If the app...Recommended model
Has daily usage with progressively unlocked valueFreemium → subscription
Delivers immediate, obvious ROI in one sessionSubscription only or one-time purchase
Produces output that varies in volume (AI, exports)Freemium + consumables
Is a simple utility used occasionallyOne-time purchase
Requires trust before users see value (health, finance)Extended free trial → subscription
Competes with free alternatives that are "good enough"Freemium (generous free tier) → subscription for power features

Process

Step 1 — Establish competitive pricing range

From competitors.json, extract pricing data for all direct competitors:

  1. List each competitor's pricing model and price point.
  2. Compute the range: competitive_min (cheapest) and competitive_max (most expensive).
  3. Identify the modal price — the price point most competitors converge on. This is the market's revealed equilibrium.
  4. Note any competitor using a different model (e.g., all use subscription but one uses one-time purchase) — this is a potential positioning gap.

If competitors.json has limited pricing data, supplement with market_insights monetization_evidence and App Store narrative pricing mentions.

Step 2 — Van Westendorp Price Sensitivity Analysis

The Van Westendorp model uses four price perception thresholds to identify the acceptable price range. Since we can't survey users directly, we estimate each threshold from available data:

The four thresholds
ThresholdQuestion (conceptual)How to estimate
Too cheap (floor)Below this price, users suspect low qualityHalf the cheapest competitor's price. If app is free, this is $0 (no floor).
Cheap but acceptable (low)Feels like a good dealCompetitive minimum price. Discount slightly if the app offers less than the cheapest competitor.
Getting expensive (target)Fair price — users would consider it but think carefullyCompetitive modal price, adjusted by desire multiplier (Step 3).
Too expensive (ceiling)Above this, users won't consider it regardless of valueCompetitive maximum price × 1.2. Cap at the "pain threshold" — the maximum users pay for similar apps in the category.
Acceptable price range

The optimal price point sits between "cheap but acceptable" and "getting expensive" — this is the target WTP.

wtp_low = cheap_but_acceptable
wtp_target = getting_expensive (adjusted by desire multiplier)
wtp_aspirational = midpoint between getting_expensive and too_expensive

Step 3 — Desire-Premium Multiplier

Desire strength from desire_scores.json directly affects pricing power. Apps that tap into primal desires command premium pricing; apps that solve mild inconveniences face price resistance.

Primary desire driverPremium multiplierRationale
Survival (health, safety, financial security)1.3–1.8×Users pay more when stakes are high. Health and money apps can charge premium.
Status (achievement, appearance, signaling)1.5–2.0×Status is inherently scarce — users pay for differentiation. Luxury/prestige positioning works.
Belonging (community, connection)1.0–1.3×Users value belonging but expect community features to be free (social norms). Premium only for exclusive communities.
Control (mastery, organization, reducing chaos)1.2–1.5×Moderate premium. Users pay for control when the alternative is stressful.
Curiosity (learning, discovery, novelty)0.8–1.2×Lowest premium. Curiosity-driven apps compete with free content (YouTube, blogs). Must add structure/accountability to charge.

Apply the multiplier to the competitive modal price:

desire_adjusted_target = competitive_modal_price × desire_multiplier

If desire_strength_label = "weak", do not apply any multiplier — the app lacks the emotional pull to justify premium pricing.

Secondary desire bonus

If the secondary desire driver is different from the primary and scores ≥ 3, add a +10% bonus. Apps that tap two distinct desires (e.g., survival + control in a health tracker) have stronger pricing power than single-desire apps.

Step 4 — WTP Benchmarks by App Category

Use these ranges as a sanity check against the Van Westendorp and desire-adjusted estimates:

App categorySubscription WTP range (monthly)One-time WTP rangeAnnual discount sweet spot
Health & fitness$5–$15/mo$8–$3040–50% off monthly
Nutrition / diet$5–$12/mo$8–$2540–50% off monthly
Meditation / mental health$5–$15/mo$10–$3050–60% off monthly
Finance / budgeting$3–$10/mo$5–$2030–40% off monthly
Productivity / task management$3–$8/mo$5–$1530–40% off monthly
Habit tracking$2–$6/mo$3–$1040–50% off monthly
Creative tools (photo/video)$3–$10/mo$5–$2030–40% off monthly
Education / learning$5–$15/mo$10–$3050–60% off monthly
Dating / social$5–$25/moN/A (subscription dominates)30–40% off monthly
Parenting / family$3–$8/mo$5–$1540–50% off monthly
Utility / scanner / converter$1–$4/mo$2–$850–60% off monthly
AI-powered tools$5–$20/mo$10–$40 consumable pack30–40% off monthly

If the estimated WTP is more than 2× above the category range, it's likely too optimistic. If it's below the category minimum, the app may struggle to sustain development.

Step 5 — Freemium Conversion Estimation

If the recommended model includes a free tier, estimate what percentage of free users will convert to paid:

FactorHigher conversion (toward 8–12%)Lower conversion (toward 1–3%)
Value gatingCore value is free; premium unlocks power features or removes limitsCore value IS the paid feature — free tier feels empty
Free tier generosityGenerous enough that users form habits before hitting the wallEither too generous (no reason to pay) or too stingy (users leave)
Pain of freeFree tier has clear, felt limitations (ads, watermarks, usage caps)Free tier works fine for most users
Social proofUsers see premium features in action (shared content with branding)No visibility into what premium offers
Trial exposureTime-limited trial of premium (7–14 days) shows value upfrontNo trial; users must imagine premium value

Category benchmarks for freemium conversion:

CategoryTypical conversion rateTop-quartile rate
Health & fitness2–5%8–12%
Productivity3–6%10–15%
Creative tools3–7%10–18%
Education2–5%7–12%
Finance3–6%8–14%
Utility1–4%5–10%
Social / dating2–8%10–20%
AI-powered4–8%12–20%

Use the typical rate as the base estimate. Adjust toward top-quartile if:

  • The desire_strength_label is "strong"
  • The free tier design has strong value gating
  • Category benchmarks from market_insights show successful freemium competitors

Step 6 — Annual vs. Monthly Strategy

Most indie apps benefit from offering both monthly and annual plans. The annual plan serves as the anchor.

Annual discount psychology
Discount depthEffect
20–30% off monthlySubtle incentive. Users who prefer monthly will stay monthly. Low commitment-capture rate.
40–50% off monthlySweet spot for most B2C apps. Enough savings to feel meaningful, not so deep it signals desperation.
50–60% off monthlyAggressive — use for high-churn categories where locking in annual users dramatically improves LTV.
> 60% off monthlySignals the app isn't worth the monthly price. Avoid unless in launch/promotional phase.

Recommended annual price = monthly price × 12 × (1 - discount). Present the annual plan as the default/highlighted option.

Step 7 — B2C vs. B2B2C Pricing

Some ideas have a viable path to both consumer and business revenue. Evaluate if the app concept could serve both:

Signal that B2B2C path existsExample
Users would expense the app to their employerProductivity tools, professional development
A business version could serve teamsShared dashboards, admin controls, team analytics
Content or data from the app has business valueHealth data for employers, fitness for corporate wellness
The consumer app is a wedge into workplace adoptionPersonal Slack → company Slack, personal Notion → team Notion

If B2B2C path exists:

  • B2C price: Use the standard WTP methodology above (this is the consumer price).
  • B2B2C price: 3–5× the consumer price per seat. Businesses have larger budgets and lower price sensitivity.
  • Recommendation: Launch B2C first (faster validation, faster feedback). Flag B2B2C as a future monetization expansion in the output.
  • Do NOT recommend B2B2C as the primary model for an indie developer — enterprise sales cycles and support needs don't fit the indie model.

Output

Write to memory/ideas/<slug>/pricing.json:

json
{
  "wtp_range": {
    "low": 0,
    "target": 0,
    "aspirational": 0,
    "currency": "USD",
    "period": "monthly | yearly | one-time"
  },
  "van_westendorp": {
    "too_cheap": 0,
    "cheap_but_acceptable": 0,
    "getting_expensive": 0,
    "too_expensive": 0
  },
  "desire_premium_multiplier": 0,
  "desire_premium_rationale": "",
  "recommended_pricing_model": "",
  "pricing_models_considered": [
    {
      "model": "",
      "fit_rationale": "",
      "risk": ""
    }
  ],
  "recommended_price": {
    "monthly": 0,
    "annual": 0,
    "annual_discount_pct": 0,
    "one_time": 0
  },
  "freemium_conversion_estimate": 0,
  "freemium_conversion_rationale": "",
  "competitive_pricing_range": {
    "min": 0,
    "max": 0,
    "modal": 0
  },
  "category_benchmark_range": {
    "min": 0,
    "max": 0
  },
  "b2b2c_potential": {
    "exists": false,
    "b2b_price_estimate": 0,
    "rationale": ""
  },
  "market_insights_pricing_signals": [],
  "pricing_rationale": ""
}

Notes

  • The target WTP is the price used by downstream skills (cac-modeler for LTV, tam-sam-som-builder for revenue projections). Getting this wrong cascades errors through the entire scoring system.
  • If monetization_evidence from market_insights is empty across all platform files, flag pricing as "unvalidated" in the rationale. This is a key risk — the market may not support paid apps.
  • For apps competing with strong free alternatives (Google Keep, Apple Notes, default apps), the effective WTP ceiling may be $0 for most users. In this case, the pricing model must create value that the free alternative structurally cannot (social features, AI, specialized workflows).
  • Lifetime deal pricing should only appear as a launch tactic, never as the recommended primary model. LTD price = 3–5× annual price. Cap at $60 for consumer apps.
  • If market_insights files are past their stale_after date, note that competitive pricing may have shifted and recommend refreshing.

Frequently asked questions

What does the Pricing And Wtp AI skill do?

Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and market_insights monetization signals. Recommends pricing model, freemium conversion estimate, and annual/monthly strategy.

Why use Pricing And Wtp on TypingMind?

Because you install it once and use it with any model. Pricing And Wtp 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 Pricing And Wtp in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pricing And Wtp?

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 Pricing And Wtp?

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

Is the Pricing And Wtp AI skill free?

Yes. It is published on GitHub by MaxKmet 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇