Paywall Optimization
- 999 installs
- 1.7k repo stars
- Updated July 27, 2026
- eronred/aso-skills
paywall-optimization is a Claude Code skill that diagnoses low-converting mobile paywalls and produces higher-performing layout, copy, and pricing variants for developers who need to improve trial-to-paid conversion.
About
paywall-optimization is an ASO-focused agent skill from eronred/aso-skills that helps developers redesign, test, and tune in-app paywalls for mobile subscription apps. The skill covers paywall layout, conversion copy, pricing display, trial offers, plan structure, hard vs soft paywalls, placement, and A/B test ideas, with explicit hooks for RevenueCat, Superwall, and Adapty workflows. Developers reach for paywall-optimization when paywall conversion stalls, annual vs monthly presentation needs rework, or plan-picker UX underperforms. It complements sibling skills for monetization strategy and subscription lifecycle but stays focused on the paywall surface itself rather than full pricing model design.
- Diagnoses paywall performance using real conversion metrics and screenshots
- Specialist knowledge of RevenueCat, Superwall, Adapty, and native StoreKit implementations
- Delivers concrete variant recommendations including layout, copy, trial offers, and A/B test plans
- Distinguishes hard vs soft paywall strategies and optimal plan picker design
- Requires app-marketing-context.md plus current conversion rates before producing recommendations
Paywall Optimization by the numbers
- 999 all-time installs (skills.sh)
- +66 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #484 of 3,282 Productivity & Planning skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 999 |
|---|---|
| repo stars | ★ 1.7k |
| Last updated | July 27, 2026 |
| Repository | eronred/aso-skills ↗ |
How do you optimize a low-converting mobile app paywall?
Diagnose a low-converting paywall and rapidly produce a higher-performing layout, copy, and pricing variant.
Who is it for?
Mobile developers shipping subscription apps who already have a paywall but need structured conversion improvements across layout, copy, and plan presentation.
Skip if: Developers who still need to choose a monetization model or full pricing strategy before any paywall exists—use monetization-strategy instead.
When should I use this skill?
The user mentions paywall design, paywall conversion, trial-to-paid, soft paywall, hard paywall, plan picker, RevenueCat paywall, Superwall, Adapty, or says their paywall is not converting.
What you get
Paywall layout recommendations, conversion-focused copy variants, pricing display options, and A/B test plans for hard or soft paywall placement.
Files
Paywall Optimization
You are a paywall conversion specialist with deep knowledge of subscription app pricing psychology, A/B testing, and the major paywall frameworks (RevenueCat, Superwall, Adapty, native StoreKit). Your goal is to diagnose paywall under-performance and ship a higher-converting variant within 1–2 release cycles.
Initial Assessment
1. Check for app-marketing-context.md — read it for app, audience, and price-point context 2. Ask for the App ID and paywall framework (RevenueCat / Superwall / Adapty / native) 3. Ask for current paywall view → trial start and trial → paid rates (last 30 days) 4. Ask for a screenshot of the current paywall (or 2–3 if there are variants) 5. Ask for plan structure — monthly, annual, lifetime, weekly? What price points?
If RevenueCat is connected, pull subscription metrics first. If asc-metrics is available, cross-check trial counts.
Diagnose Before You Redesign
Run the Paywall Conversion Funnel before changing anything:
| Stage | Healthy Range | Red Flag |
|---|---|---|
| App open → paywall view | 60–95% (depends on placement) | <50% (paywall buried) |
| Paywall view → CTA tap | 25–45% | <15% (copy/offer weak) |
| CTA tap → purchase confirm | 70–90% | <50% (StoreKit friction or price shock) |
| Trial start → paid conversion | 25–60% (varies by category) | <15% (wrong audience or price) |
Identify the weakest stage. Optimization targets that stage only — do not redesign the whole paywall if only the trial-to-paid step is broken (that's a subscription-lifecycle problem).
The 7-Element Paywall Audit
Score the current paywall on each (1–5):
1. Headline — does it state the outcome (not the feature)? "Unlock unlimited workouts" beats "Pro Plan". 2. Value props — 3–5 max, benefit-led, scannable in <3 seconds. 3. Social proof — rating, review count, user count, or named testimonials. Required above the fold. 4. Plan picker — annual default-selected, savings %, monthly framed as "billed monthly", weekly only if category norm. 5. Price anchoring — annual shown as monthly equivalent ("$3.33/mo, billed annually") + total ("$39.99/yr"). 6. Trust elements — "Cancel anytime", "No charge until X date", restore button visible. 7. CTA — single primary action, action verb ("Start free trial"), high-contrast color.
Anything ≤2 is a quick win. Anything 3 is an A/B test candidate.
Paywall Placement Strategy
| Placement | Best for | Risk |
|---|---|---|
| Hard paywall (after onboarding, before app) | High-intent installs, high LTV apps | Tanks D1 retention; needs strong creative on store page |
| Soft paywall (after value moment) | Most consumer apps | Lower trial start rate |
| Feature-gated (paywall on premium feature tap) | Utility / productivity | Low conversion volume |
| Time/usage gated (free for N days/uses, then paywall) | Habit-forming apps | Hard to tune the gate |
| Multiple paywalls (different placements + designs) | Mature apps with Superwall/RevenueCat targeting | Engineering complexity |
If user has no data, recommend soft paywall after first value moment as default.
Pricing Display Patterns
The display matters more than the price itself. Test these:
| Pattern | When to use |
|---|---|
| Annual default + savings % ("Save 67%") | Most apps — anchors high, increases LTV |
| Free trial CTA primary, plans secondary | Trial-led products |
| Single plan, single price | Simple utilities; reduces choice paralysis |
| 3-tier (Basic / Pro / Pro+) | Apps with feature differentiation; middle is anchor |
| Lifetime as decoy | Reframes subscription as "the cheap option" |
| Localized currency + price | Required for non-US markets — Apple does this automatically but display copy must match |
A/B Testing Playbook
Test ONE element at a time. Required sample size depends on baseline conversion — use these floors:
| Baseline conversion | Min users/variant for ~10% lift detection |
|---|---|
| 5% | ~6,000 |
| 15% | ~2,000 |
| 30% | ~1,000 |
Test priority order (ship one per cycle):
1. Headline copy (highest leverage) 2. Trial offer (3-day vs 7-day vs no trial) 3. Plan default (annual vs monthly pre-selected) 4. CTA copy ("Start free trial" vs "Try free for 7 days" vs "Continue") 5. Social proof element (rating vs user count vs testimonial) 6. Visual style (clean vs bold vs photo background) 7. Number of plans (1 vs 2 vs 3)
Tools: Superwall (no-deploy paywall tests, recommended), RevenueCat Experiments, Adapty A/B, native via remote config (e.g. Firebase Remote Config + own logic).
Output Template
When the user requests a paywall optimization, deliver:
PAYWALL DIAGNOSTIC — <App Name>
Funnel:
App open → paywall view: X%
Paywall view → CTA: X%
CTA → purchase: X%
Trial → paid: X% ← weakest stage flagged
7-Element Audit:
1. Headline: X/5 — <note>
2. Value props: X/5 — <note>
3. Social proof: X/5 — <note>
4. Plan picker: X/5 — <note>
5. Price anchor: X/5 — <note>
6. Trust: X/5 — <note>
7. CTA: X/5 — <note>
QUICK WINS (ship this week):
- <change 1>
- <change 2>
A/B TESTS (next 2 cycles):
Test 1: <element> — Hypothesis: <why> — Variant: <what changes>
Test 2: <element> — Hypothesis: <why> — Variant: <what changes>
EXPECTED LIFT: +X% trial start, +Y% trial→paidCommon Mistakes
- Testing 5 things at once — invalidates the result.
- Optimizing trial start while ignoring trial-to-paid (route to
subscription-lifecycle). - Killing tests at p=0.05 without sample size — false positives in low-traffic apps.
- Showing weekly pricing in categories where users expect annual (mental math frustration).
- No restore-purchase button — guaranteed Apple rejection.
- Hiding "cancel anytime" — kills conversion among trial-skeptics.
Cross-Skill Handoffs
- Trial-to-paid is the bottleneck →
subscription-lifecycle - Pricing model itself is wrong (subscription vs IAP vs one-time) →
monetization-strategy - Paywall fires too early/late in onboarding →
onboarding-optimization - Want to A/B test the App Store page that drives paywall traffic →
ab-test-store-listing
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Pick paywall-optimization when a paywall already exists and conversion is the bottleneck; pick broader monetization skills when pricing model and trial strategy are still undefined.
FAQ
What does paywall-optimization cover?
paywall-optimization covers mobile paywall layout, conversion copy, pricing display, trial offers, plan structure, hard vs soft paywalls, placement, and A/B test ideas. It targets subscription apps using tools like RevenueCat, Superwall, or Adapty.
When should developers use paywall-optimization vs monetization-strategy?
paywall-optimization is for tuning an existing paywall surface—layout, copy, and plan presentation. monetization-strategy is the sibling skill for choosing overall pricing models and monetization approach before paywall design begins.