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Get Started Free →The playbook for what you charge and how you present it: value metric, tiers, anchoring, display psychology, discounts and promos, add-ons, trial models, and high-ticket or subscription structures. Use whenever the user is setting or fixing prices, designing a pricing page or tiers, or says things like "how much should I charge", "what should my tiers be", "how do I raise prices", "should I do a free trial", "annual vs monthly", "my pricing page isn't converting", "add a discount", "increase ARP
.claude/skills/whatsuppiyush-pricing-monetization/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | 324% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 391% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 351% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 295% | 0% |
| case-23 | ✗→✓ | ▲ Improved | 287% | 0% |
The discipline of capturing the value you create: pick the right unit to charge on, structure tiers that hold, present the number so it reads as fair, and add the trial, discount, add-on, and plan mechanics that lift ARPU and LTV without training buyers to wait for a sale. The model and the framing matter more than the digit itself.
Reach for this skill when the work touches price, packaging, or how either is presented:
Trigger phrases: "how much should I charge", "what tiers", "raise my prices", "free trial length", "reverse trial", "annual discount", "usage-based pricing", "value metric", "anchor pricing", "decoy tier", "charm pricing", "add-ons", "increase ARPU/LTV", "high-ticket offer", "sell a premium product".
proof, reciprocity as mechanisms) are catalogued in marketing-psychology. This skill applies them to price; go there to understand or extend the principle itself.
(beyond the price-display specifics here) is conversion-cro. Come here for what the tiers and numbers should be; go there to A/B test and optimize the funnel around them.
Build the model first, then the tiers, then the framing, then the promotions. Skipping to discounts before the model is set trains the wrong buyers.
and is instantly clear, and match your pricing friction to your product friction (value-metric-pricing.md). Get this wrong and every tier below inherits the flaw.
buyers cannot self-downgrade, and steer most to the middle (pricing-page-and-tiers.md). Price annual off real retention math, not a blanket "2 months free" (annual-plan-pricing.md).
comparison set, plant a decoy, frame the cost of not buying, anchor high (pricing-anchoring.md), then stack the display tactics on the actual UI (pricing-display-psychology.md).
trial that starts users on premium then downgrades (free-trial-models.md).
only discount the segments that actually need a nudge (discount-promo-mechanics.md); add scarcity and collectible variants where a fanbase exists (mystery-box-offers.md).
(add-on-unbundling.md); add group/family and mid-term plans (subscription-plan-structures.md); add a take-back/buy-back promise to lift willingness-to-pay (take-back-buyback.md).
offer engineered on the Value Equation (high-ticket-offer-design.md); justify a premium price with replacement positioning and scaled proof (ag1-premium-playbook.md).
Diagnostic shortcut: unit economics break as customers grow, fix the value metric. Buyers downgrade to the cheapest tier, add a fence. Page has traffic but weak conversion, work anchoring and display. Revenue per account is flat, look at add-ons and plan structures. Product is easy to sell cheap but you want more per customer, go high-ticket first.
candidate metrics on alignment, scalability, clarity. Match pricing friction to product friction: one-click-to-try products want free/freemium, high-touch products want premium/sales-assisted.
roughly Good 10 to 20% / Better 25 to 50% / Best 30 to 60%. A fence attribute (HBO Max used ads) can push about 90% to the higher tier. Highlight 3 to 10 features per tier, 2 to 4 tiers.
targets an annual monthly-equivalent about one-third of standalone monthly. Vail shifted annual adoption 35% to 75% and modeled +61% cumulative revenue by year 2.
Rule-of-40% add-ons drove +12 to 22% revenue and +18 to 54% LTV. An à-la-carte ladder can roughly 2x ARPU.
trials lift freemium conversion about 10 to 40%.
show % off under $100, $ off over $100. Cap targeted discounts around 20 to 25% and keep timing unpredictable. Info-product price-hiking: one ebook started $10, +$5 per 30 units, sold 3,400 copies for $130k+.
for value, round for luxury. Frame as "free" over "50% off" where a component can be given away. A repackaged unit anchor ("18 pieces") lifted Snickers sales 38%.
done-for-you product earned about 10x revenue per customer vs self-serve. Value Equation = (Dream Outcome × Perceived Likelihood) ÷ (Time Delay × Effort).
social. Take-back/buy-back lifted willingness-to-pay +39.1% (a pen) and +12.2% (an IKEA armchair); brand loyalty rose +13 to 19%.
Every play above, in full.
Features that only a minority of users touch are dragging down your base-tier price for everyone else. Pull those minority-use features out as paid add-ons (while keeping them available), and let power users assemble an à-la-carte stack that can double ARPU without raising the entry price.
A SaaS or subscription product with a broad feature set where some capabilities (integrations, analytics, priority support, advanced modules) are used by a minority of accounts.
Strong skill candidate: an "add-on opportunity finder" that takes a feature-usage table, flags every feature under the 40% threshold as an add-on candidate, and models the ARPU lift of an à-la-carte bundle.
AG1 sells a ~$79/mo greens powder in a commodity category by making the price feel like a bargain replacement for a whole category of products, rebranding the category itself, and stacking overwhelming social proof and premium signals. It's a repeatable 7-part template for charging a premium in a crowded market.
Positioning a premium-priced product in a category where cheaper alternatives exist and buyers need a reason to pay more.
Moderate skill value (a positioning template, not a calculator); could become a "premium positioning worksheet" that walks a product through the 7 levers and drafts replacement-positioning + category-rebrand copy.
Most companies price annual plans as a flat "2 months free" (~15-20% off) and show the monthly plan first. Two better moves: (1) set the annual price from actual retention math so you capture LTV instead of guessing a discount, and (2) widen the annual-vs-monthly gap dramatically ("anchor shock", annual monthly-equivalent ≈ 1/3 of the standalone monthly price) and show the annual monthly-equivalent FIRST so the standalone monthly looks like the expensive outlier.
Subscription products (SaaS, memberships, consumer apps) with meaningful monthly churn where you want to push users onto annual for cash flow, retention, and higher LTV. NOT for low-retention, luxury, or enterprise/negotiated contracts.
Strong skill candidate: an "annual plan pricing calculator" that takes monthly price + average retention (or a competitor's monthly/annual pair) and outputs a retention-based annual price, an anchor-shock price, the implied break-even in uses, and pricing-page display copy.
Discounts leak margin when they're blanket and forgettable. Jonah Berger's viral-promo mechanics make a discount spread and convert; segment-aware discounting keeps you from paying people who'd have bought anyway; and info-product price-hiking turns a discount into a rising-price event.
Running a sale, launch promo, seasonal campaign, or setting a standing discount policy. Especially for DTC/ecommerce and info products.
Strong skill candidate: a "promo mechanics generator" that outputs a specific scarcity cap, an unrounded countdown, a Rule-of-100 discount format, and a segment-targeting rule for a given product and margin.
Two trial mechanics beat the default 14/30-day free trial. A shorter 7-day trial creates urgency and denser usage; a reverse trial gives users the full premium experience first, then downgrades them to free, using loss aversion to drive upgrades.
Subscription products deciding trial length or freemium-to-paid conversion structure.
Moderate skill candidate: a "trial model chooser" that recommends 7-day vs reverse-trial based on freemium fallback strength, plus an onboarding checklist to hit aha inside the window.
Counterintuitively, products that are hard to fulfill are easier to sell (and command far higher prices), while easy-to-fulfill self-serve products are hard to sell. Lead with a premium done-for-you offer, engineered with the Value Equation, then productize downward later.
Early-stage or services-adjacent products deciding whether to launch self-serve/cheap or high-touch/expensive; designing a high-ticket offer.
Strong skill candidate: an "offer builder" that scores a draft offer on the four Value Equation inputs, suggests guarantees/risk-reversal to lift likelihood and DFY elements to cut effort/time, and recommends a DFY-first vs self-serve-first launch order.
Packaging a product as a mystery box with collectible variants exploits completionism and curiosity: superfans buy every variant to "catch them all," pushing order value far above the base product and driving fast sellouts.
Ecommerce, merch, creator drops, or paid programs with a passionate fanbase and products that can come in multiple collectible variants.
Lower skill value (a promotional format); could become a "mystery drop planner" that structures variants, supply caps, and pricing so the complete-the-set price clears a target AOV.
Price is judged relative to whatever context you put around it, not in absolute terms. You can move perceived value without changing the number by controlling the comparison set, planting a decoy, framing the cost of inaction, and ordering options high-to-low.
Any pricing page, sales page, or offer where buyers lack an obvious reference price, new categories, premium products, info products, DTC.
Good skill candidate: a "pricing anchor auditor" that takes a product + price and suggests a higher comparison category, a decoy tier, a cost-of-inaction frame, and an option ordering.
How a price is rendered changes how expensive it feels, independent of the number. Stack a set of well-studied display tricks on your pricing UI to lower perceived cost and raise perceived popularity.
Finalizing the visual design of a pricing page, product page, or checkout. These are layout/typography/color decisions, not pricing-model changes.
Strong skill candidate: a "pricing display linter" that reviews a pricing screenshot/markup and flags violations (currency symbol prominence, mismatched font sizes, wrong ending style for the positioning, Rule-of-100 discount format).
Tiered pricing works when each tier has a clear "fence" that makes downgrading feel like a real sacrifice, and when the page is built to sell benefits and route most buyers to your target tier, not to list features.
Building or refactoring a SaaS/subscription pricing page with 2-4 tiers.
Strong skill candidate: a "tier designer" that takes a feature list + target ARPU and proposes 3 tiers with a fence attribute per boundary, spacing within the ≤25%/≤50% rules, a target split, and a benefit-led table layout.
Beyond monthly and annual, two plan structures unlock growth: a discounted group/family plan that turns customers into recruiters and makes cancelling socially costly, and mid-term (quarterly/semi-annual) plans that fit seasonal or non-replenishment products where annual is too big a commitment.
Subscription products (consumer apps, boxes, memberships) looking to add plan variety to lift acquisition, retention, and cash-flow predictability.
Lower skill value (a plan-menu decision aid); could become a short recommender that suggests group and mid-term plan options based on product type and consumption pattern.
Offering to take back or buy back a product at end-of-life measurably increases what customers will pay for it up front and lifts brand loyalty, the promise of residual value and reduced waste-guilt makes the purchase feel lower-risk.
Durable physical goods (furniture, apparel, electronics, tools) where resale, recycling, or trade-in is feasible and sustainability matters to buyers.
Lower skill value (a program-design prompt); could become a checklist for structuring a credible take-back offer and placing it at the point of sale.
The unit you charge on (the "value metric") should track the value a customer gets, scale as they grow, and be easy to understand. Choosing it deliberately, and matching your pricing model to how much friction your product has, is more important than the number on the tier.
Deciding what to charge per (seat, contact, email, course, GB…) for a new product, or fixing a pricing model that punishes growth or confuses buyers.
Strong skill candidate: a "value metric finder" that takes a product's JTBD, proposes candidate metrics, scores them on alignment/scalability/clarity, and recommends a pricing-friction model (free/freemium vs premium/high-touch).
From God of Skills: a curated, hand-tested directory of AI skills, prompts, templates and image style guides. Source: https://godofskills.com/skills/pricing-monetization?ref=claude-skill
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-06 | pass→pass | 9,166 | 5,223 | -43% | 1 | 1 | 0% | 1,708 | 9,248 | +441% | 0 | 0 | — |
case-16 | fail→fail | 15,386 | 11,380 | -26% | 1 | 1 | 0% | 2,786 | 10,314 | +270% | 0 | 0 | — |
case-01 | fail→fail | 24,033 | 18,139 | -25% | 1 | 1 | 0% | 4,038 | 11,449 | +184% | 0 | 0 | — |
case-02 | pass→pass | 20,439 | 11,402 | -44% | 1 | 1 | 0% | 3,354 | 10,320 | +208% | 0 | 0 | — |
case-03 | pass→pass | 19,827 | 20,625 | +4% | 1 | 1 | 0% | 3,612 | 12,254 | +239% | 0 | 0 | — |
case-04 | pass→pass | 7,920 | 6,919 | -13% | 1 | 1 | 0% | 1,326 | 9,438 | +612% | 0 | 0 | — |
case-05 | fail→fail | 13,757 | 6,671 | -52% | 1 | 1 | 0% | 2,497 | 9,606 | +285% | 0 | 0 | — |
case-07 | pass→pass | 6,383 | 5,270 | -17% | 1 | 1 | 0% | 1,324 | 9,322 | +604% | 0 | 0 | — |
case-08 | pass→pass | 16,752 | 7,620 | -55% | 1 | 1 | 0% | 2,514 | 9,671 | +285% | 0 | 0 | — |
case-09 | pass→pass | 15,040 | 8,985 | -40% | 1 | 1 | 0% | 2,573 | 9,822 | +282% | 0 | 0 | — |
case-10 | pass→pass | 14,725 | 9,182 | -38% | 1 | 1 | 0% | 2,517 | 9,686 | +285% | 0 | 0 | — |
case-11 | pass→pass | 7,967 | 10,179 | +28% | 1 | 1 | 0% | 1,566 | 9,837 | +528% | 0 | 0 | — |
case-12 | fail→pass | 13,155 | 9,034 | -31% | 1 | 1 | 0% | 2,255 | 9,567 | +324% | 0 | 0 | — |
case-13 | pass→pass | 15,319 | 9,890 | -35% | 1 | 1 | 0% | 2,552 | 9,978 | +291% | 0 | 0 | — |
case-14 | fail→pass | 11,641 | 8,729 | -25% | 1 | 1 | 0% | 1,990 | 9,769 | +391% | 0 | 0 | — |
case-15 | fail→pass | 11,241 | 6,674 | -41% | 1 | 1 | 0% | 2,103 | 9,488 | +351% | 0 | 0 | — |
case-17 | pass→pass | 15,940 | 9,445 | -41% | 1 | 1 | 0% | 2,552 | 10,023 | +293% | 0 | 0 | — |
case-18 | fail→pass | 13,906 | 4,938 | -64% | 1 | 1 | 0% | 2,292 | 9,062 | +295% | 0 | 0 | — |
case-19 | pass→pass | 14,595 | 8,004 | -45% | 1 | 1 | 0% | 2,419 | 9,837 | +307% | 0 | 0 | — |
case-20 | pass→pass | 16,873 | 11,792 | -30% | 1 | 1 | 0% | 2,805 | 10,617 | +279% | 0 | 0 | — |
case-21 | pass→pass | 15,135 | 12,286 | -19% | 1 | 1 | 0% | 2,300 | 10,654 | +363% | 0 | 0 | — |
case-22 | pass→pass | 10,797 | 4,234 | -61% | 1 | 1 | 0% | 1,716 | 9,005 | +425% | 0 | 0 | — |
case-23 | fail→pass | 12,058 | 4,290 | -64% | 1 | 1 | 0% | 2,358 | 9,120 | +287% | 0 | 0 | — |
case-24 | fail→pass | 14,125 | 7,435 | -47% | 1 | 1 | 0% | 2,332 | 9,697 | +316% | 0 | 0 | — |
case-25 | pass→pass | 9,238 | 7,185 | -22% | 1 | 1 | 0% | 1,716 | 9,727 | +467% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 25 cases were attempted. The headline lift of +24 percentage points is the difference between those two pass rates over the 25 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.