Competitive Pricing Strategy
nexscope-ai/eCommerce-Skills
Build an evidence-based competitive pricing strategy for ecommerce products.
Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and marketinsights monetization signals.
Install Pricing And Wtp
$ npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install MaxKmet/idea-validation-agents pricing-and-wtp --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/pricing-and-wtp .claude/skills/pricing-and-wtp && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "pricing-and-wtp" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp into .claude/skills/pricing-and-wtp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pricing-and-wtp", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtpType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install MaxKmet/idea-validation-agents pricing-and-wtp --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/pricing-and-wtp .agents/skills/pricing-and-wtp && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pricing-and-wtp" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp into .agents/skills/pricing-and-wtp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pricing-and-wtp", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install MaxKmet/idea-validation-agents pricing-and-wtp --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/pricing-and-wtp .cursor/skills/pricing-and-wtp && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "pricing-and-wtp" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp into .cursor/skills/pricing-and-wtp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pricing-and-wtp", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/MaxKmet/idea-validation-agents.git --path skills/pricing-and-wtp--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install MaxKmet/idea-validation-agents pricing-and-wtp --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/pricing-and-wtp .gemini/skills/pricing-and-wtp && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "pricing-and-wtp" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp into .gemini/skills/pricing-and-wtp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pricing-and-wtp", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install MaxKmet/idea-validation-agents pricing-and-wtpInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/pricing-and-wtp .github/skills/pricing-and-wtp && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "pricing-and-wtp" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp into .github/skills/pricing-and-wtp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pricing-and-wtp", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install MaxKmet/idea-validation-agents pricing-and-wtp --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/MaxKmet/idea-validation-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/pricing-and-wtp .opencode/skills/pricing-and-wtp && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "pricing-and-wtp" agent skill from https://github.com/MaxKmet/idea-validation-agents/tree/main/skills/pricing-and-wtp into .opencode/skills/pricing-and-wtp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pricing-and-wtp", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
pricing-and-wtp- GitHub stars
- 478
- Token cost
- ~3.7k tokens
- SKILL.md length
- 1,716 words
- Files
- 1
- Skills in repo
- 15
- Repo updated
- First seen
- Licence
- MIT
At a glance
Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and marketinsights monetization signals.
- Works in 7 steps: Establish competitive pricing range → Van Westendorp Price Sensitivity Analysis → Desire-Premium Multiplier → …
- Tasks that involve Pricing strategy
- SKILL.md covers Purpose, Input, Pricing Models and Process, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Pricing And Wtp is an agent skill from MaxKmet/idea-validation-agents. Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and marketinsights monetization signals. Recommends pricing model, freemium conversion estimate, and annual/monthly strategy.
Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Sales & Support, covering Pricing strategy. It works with Reddit and TikTok. The repository describes itself as: AI agents that act as your personal venture analyst - from startup idea brainstorming to full validation and go-to-market strategy. Built for developers who'd rather validate in… The licence is MIT.
When your agent uses it
- Tasks that involve Pricing strategy
Example prompts
- “Use the pricing-and-wtp skill to model willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category…”
- “/pricing-and-wtp”
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 3a4c800. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Pricing And Wtp loads about 3.7k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 1,716 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from MaxKmet/idea-validation-agents at commit 3a4c800, republished under its MIT licence (© MaxKmet). 1,716 words, ~3,723 tokens.
Download SKILL.mdSave it as
.claude/skills/pricing-and-wtp/SKILL.md (or your agent's skills folder).- name
- pricing-and-wtp
- description
- 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.
<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/pricing.json -->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 $12K/year.
Input
- Idea slug
memory/ideas/<slug>/idea.md(app concept, key features)memory/ideas/<slug>/user_extraction.json(pain intensity)memory/ideas/<slug>/competitors.json(competitive pricing, pricing models in use)memory/ideas/<slug>/desire_scores.json(desire strength, primary/secondary drivers)memory/market_insights/<niche>-*-<YYYY>-<MM>.md(trend data — use all available platform files)
Using Market Insights
Trend analysis files provide monetization reality-checks that desk research alone cannot:
| Field | How it informs pricing |
|---|---|
monetization_evidence | The strongest signal. If competitors are visibly making money (in-app purchases mentioned in reviews, subscription pricing in App Store listings, sponsored content), the market has validated WTP. If monetization_evidence is empty across all platforms, WTP is unproven — discount aspirational price and flag risk. |
| Platform narratives — Reddit | Users discuss pricing directly: "I'd pay for X if...", "Y is overpriced because...", "$Z/mo is too much for just..." These are raw WTP signals. Extract any price mentions. |
| Platform narratives — App Store | Review 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 — TikTok | Creator 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_velocity | Rising-fast markets can support premium pricing (early adopters pay more). Declining markets face price pressure (users comparison-shop harder). |
Pricing Models
| Model | Best for | Risk | Indie sweet spot |
|---|---|---|---|
| Freemium → subscription | Habit-forming apps with daily use, feature-gated value | Conversion friction; most users stay free forever | $3–$8/mo or $20–$50/yr |
| Subscription only (paywall) | High-value, immediately obvious ROI, professional tools | High churn risk; must prove value fast | $5–$15/mo |
| One-time purchase | Utilities, tools with clear one-time value, privacy-focused apps | No recurring revenue; must rely on new users | $3–$10 |
| Consumables / credits | Variable usage (AI generations, exports, premium content) | Unpredictable revenue; usage may decline | $1–$5 per credit pack |
| Freemium + consumables | Apps where core is free but power usage costs (AI, storage) | Complex to balance free vs. paid tiers | Free tier + $2–$10 packs |
| Lifetime deal | Launch phase only — use for early adopter cash and reviews | Destroys LTV; never use as primary model | $20–$60 (3–5× annual price) |
| Tip jar / patronage | Content-focused, community-driven, or open-source-adjacent | Very low conversion; unreliable revenue | $1–$5 tips, rare |
Model selection criteria
| If the app... | Recommended model |
|---|---|
| Has daily usage with progressively unlocked value | Freemium → subscription |
| Delivers immediate, obvious ROI in one session | Subscription only or one-time purchase |
| Produces output that varies in volume (AI, exports) | Freemium + consumables |
| Is a simple utility used occasionally | One-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:
- List each competitor's pricing model and price point.
- Compute the range:
competitive_min(cheapest) andcompetitive_max(most expensive). - Identify the modal price — the price point most competitors converge on. This is the market's revealed equilibrium.
- 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
| Threshold | Question (conceptual) | How to estimate |
|---|---|---|
| Too cheap (floor) | Below this price, users suspect low quality | Half the cheapest competitor's price. If app is free, this is $0 (no floor). |
| Cheap but acceptable (low) | Feels like a good deal | Competitive minimum price. Discount slightly if the app offers less than the cheapest competitor. |
| Getting expensive (target) | Fair price — users would consider it but think carefully | Competitive modal price, adjusted by desire multiplier (Step 3). |
| Too expensive (ceiling) | Above this, users won't consider it regardless of value | Competitive 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_expensiveStep 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 driver | Premium multiplier | Rationale |
|---|---|---|
| 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_multiplierIf 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 category | Subscription WTP range (monthly) | One-time WTP range | Annual discount sweet spot |
|---|---|---|---|
| Health & fitness | $5–$15/mo | $8–$30 | 40–50% off monthly |
| Nutrition / diet | $5–$12/mo | $8–$25 | 40–50% off monthly |
| Meditation / mental health | $5–$15/mo | $10–$30 | 50–60% off monthly |
| Finance / budgeting | $3–$10/mo | $5–$20 | 30–40% off monthly |
| Productivity / task management | $3–$8/mo | $5–$15 | 30–40% off monthly |
| Habit tracking | $2–$6/mo | $3–$10 | 40–50% off monthly |
| Creative tools (photo/video) | $3–$10/mo | $5–$20 | 30–40% off monthly |
| Education / learning | $5–$15/mo | $10–$30 | 50–60% off monthly |
| Dating / social | $5–$25/mo | N/A (subscription dominates) | 30–40% off monthly |
| Parenting / family | $3–$8/mo | $5–$15 | 40–50% off monthly |
| Utility / scanner / converter | $1–$4/mo | $2–$8 | 50–60% off monthly |
| AI-powered tools | $5–$20/mo | $10–$40 consumable pack | 30–40% 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.
Show full SKILL.md (632 more words)Show less
Step 5 — Freemium Conversion Estimation
If the recommended model includes a free tier, estimate what percentage of free users will convert to paid:
| Factor | Higher conversion (toward 8–12%) | Lower conversion (toward 1–3%) |
|---|---|---|
| Value gating | Core value is free; premium unlocks power features or removes limits | Core value IS the paid feature — free tier feels empty |
| Free tier generosity | Generous enough that users form habits before hitting the wall | Either too generous (no reason to pay) or too stingy (users leave) |
| Pain of free | Free tier has clear, felt limitations (ads, watermarks, usage caps) | Free tier works fine for most users |
| Social proof | Users see premium features in action (shared content with branding) | No visibility into what premium offers |
| Trial exposure | Time-limited trial of premium (7–14 days) shows value upfront | No trial; users must imagine premium value |
Category benchmarks for freemium conversion:
| Category | Typical conversion rate | Top-quartile rate |
|---|---|---|
| Health & fitness | 2–5% | 8–12% |
| Productivity | 3–6% | 10–15% |
| Creative tools | 3–7% | 10–18% |
| Education | 2–5% | 7–12% |
| Finance | 3–6% | 8–14% |
| Utility | 1–4% | 5–10% |
| Social / dating | 2–8% | 10–20% |
| AI-powered | 4–8% | 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 depth | Effect |
|---|---|
| 20–30% off monthly | Subtle incentive. Users who prefer monthly will stay monthly. Low commitment-capture rate. |
| 40–50% off monthly | Sweet spot for most B2C apps. Enough savings to feel meaningful, not so deep it signals desperation. |
| 50–60% off monthly | Aggressive — use for high-churn categories where locking in annual users dramatically improves LTV. |
| > 60% off monthly | Signals 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 exists | Example |
|---|---|
| Users would expense the app to their employer | Productivity tools, professional development |
| A business version could serve teams | Shared dashboards, admin controls, team analytics |
| Content or data from the app has business value | Health data for employers, fitness for corporate wellness |
| The consumer app is a wedge into workplace adoption | Personal 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:
{
"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
targetWTP is the price used by downstream skills (cac-modelerfor LTV,tam-sam-som-builderfor revenue projections). Getting this wrong cascades errors through the entire scoring system. - If
monetization_evidencefrom 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_afterdate, note that competitive pricing may have shifted and recommend refreshing.
© MaxKmet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/pricing-and-wtp of MaxKmet/idea-validation-agents.
Open the folder on GitHubat commit 3a4c800
Compare with similar skills
Pricing And Wtp next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Pricing And Wtp this skillMaxKmet/idea-validation-agents | 478 | — | ~3.7k | Automated safety check: Pass | MIT | |
| Competitive Pricing Strategynexscope-ai/eCommerce-Skills | 1.1k | — | ~2.8k | Automated safety check: Pass | MIT | |
| Social Media Monitornexscope-ai/eCommerce-Skills | 1.1k | — | ~584 | Automated safety check: Pass | MIT | |
| Tiktok Shop Pricingnexscope-ai/eCommerce-Skills | 1.1k | — | ~442 | Automated safety check: Pass | MIT | |
| Price Optimization Toolnexscope-ai/eCommerce-Skills | 1.1k | — | ~3.3k | Automated safety check: Pass | MIT | |
| Paid Ads AuditAgriciDaniel/claude-ads | 9.9k | — | ~1.5k | Automated safety check: Pass | MIT |
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Decision Memo
MaxKmet/idea-validation-agents
Writes a concise, human-readable decision brief summarizing the full validation analysis — including score, verdict, RAT experiment, pre-mortem, and tier-appropriate next actions.
Desire Evaluator
MaxKmet/idea-validation-agents
Scores the strength of core human desire motivations (survival, status, belonging, control, curiosity) for a given app idea to predict user pull and retention potential.
Distribution Analysis
MaxKmet/idea-validation-agents
Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea.
Idea Scoring
MaxKmet/idea-validation-agents
Aggregates all dimension scores into a final idea score (0–100) and issues a verdict.
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Categories
Questions about Pricing And Wtp
What does Pricing And Wtp do?
Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and marketinsights monetization signals. Pricing And Wtp is an agent skill from MaxKmet/idea-validation-agents. Models willingness to pay using Van Westendorp price sensitivity analysis, desire-premium multipliers, category benchmarks, and marketinsights monetization signals.
When should I use Pricing And Wtp?
Pricing And Wtp fits situations like: tasks that involve Pricing strategy.
How do I install Pricing And Wtp in Claude Code?
Run `npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a claude-code`. Or copy the skill folder (skills/pricing-and-wtp in MaxKmet/idea-validation-agents) into .claude/skills/pricing-and-wtp in your project. Claude Code loads it when a task matches its description.
How do I install Pricing And Wtp in Codex?
Run `npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a codex`. Or copy the skill folder (skills/pricing-and-wtp in MaxKmet/idea-validation-agents) into .agents/skills/pricing-and-wtp in your project. Codex loads it when a task matches its description.
Can I use Pricing And Wtp in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add MaxKmet/idea-validation-agents --skill pricing-and-wtp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pricing-and-wtp, .gemini/skills/pricing-and-wtp, .github/skills/pricing-and-wtp and .opencode/skills/pricing-and-wtp in your project.
What does Pricing And Wtp need to run?
SKILL.md names no scripts, command-line tools or credentials: Pricing And Wtp is instructions for the agent only.
Does Pricing And Wtp access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Pricing And Wtp safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Pricing And Wtp use?
Pricing And Wtp is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Pricing And Wtp use?
About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Pricing And Wtp?
Skills that share tags, products or a category with Pricing And Wtp: Competitive Pricing Strategy (nexscope-ai/eCommerce-Skills, 1.1k stars), Social Media Monitor (nexscope-ai/eCommerce-Skills, 1.1k stars), Tiktok Shop Pricing (nexscope-ai/eCommerce-Skills, 1.1k stars) and Price Optimization Tool (nexscope-ai/eCommerce-Skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pricing And Wtp?
MaxKmet (a GitHub user) maintains it in MaxKmet/idea-validation-agents, which has 478 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on June 16, 2026.
Source: MaxKmet/idea-validation-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.