paywall-optimization
by EronredThe paywall-optimization skill helps you diagnose and improve subscription paywalls for conversion. Use it when the problem is layout, copy, pricing display, trial framing, plan structure, placement, or paywall A/B tests. It’s designed for RevenueCat, Superwall, Adapty, and native StoreKit flows, and it helps separate paywall issues from broader monetization or onboarding problems.
This skill scores 78/100, which means it is a solid listing candidate for directory users: it gives a clear trigger, a defined workflow, and enough operational detail to help an agent optimize a paywall with less guesswork than a generic prompt. Users should still expect some adoption friction because it lacks companion files and has a test-like signal, but the core skill content is substantive and install-worthy.
- Very clear trigger coverage for paywall design, pricing display, trial offers, paywall placement, and A/B tests, which makes it easy for agents to know when to use it.
- Operational workflow is explicit: it asks for app context, framework, conversion rates, screenshots, and plan structure before advising, reducing ambiguity.
- Strong domain leverage: it names major paywall systems (RevenueCat, Superwall, Adapty, native StoreKit) and frames the goal as shipping a higher-converting variant within 1–2 release cycles.
- No install command, scripts, references, or resources are provided, so users get the core workflow but not much supporting automation or external guidance.
- The repository includes an experimental/test signal and no companion files, so users should verify fit for their app’s stack and analytics setup before relying on it.
Overview of paywall-optimization skill
What this skill does
The paywall-optimization skill helps you diagnose and improve subscription paywalls for conversion, not just rewrite them. It is useful when you need to decide whether the issue is layout, copy, pricing display, trial framing, plan structure, placement, or a weak test hypothesis.
Best-fit use cases
Use the paywall-optimization skill when you are working on a subscription app and need better paywall performance across tools like RevenueCat, Superwall, Adapty, or native StoreKit. It is a strong fit for teams asking why a paywall is not converting, how to compare annual vs monthly presentation, or how to design a cleaner A/B test.
What makes it different
This skill is built around diagnosis first, redesign second. That matters because many paywall problems come from poor offer structure or bad placement, not just weak wording. The paywall-optimization guide also helps separate conversion issues from broader monetization strategy or onboarding flow problems.
How to Use paywall-optimization skill
Install and open the right files
Run the paywall-optimization install flow with the repository path, then start with SKILL.md. If you are using the repo directly, the first file to read is skills/paywall-optimization/SKILL.md, because it contains the working process, intake questions, and decision order for the skill.
Give the skill the right input
For useful paywall-optimization usage, do not ask for “a better paywall” in the abstract. Provide the app category, framework, current paywall screenshot, price points, trial length, and the current funnel numbers: paywall view to trial start, and trial start to paid. If you already know the problem, say so clearly, such as “low trial starts on annual-first paywall” or “monthly wins, but ARPU is too low.”
Turn a rough request into a strong prompt
A stronger request looks like: “Review this RevenueCat paywall for a meditation app. We have 12% paywall view to trial start, 38% trial to paid, one monthly and one annual plan, and the annual plan is visually dominant but not winning. Suggest the highest-impact changes and a test plan.” This gives the skill enough context to make a conversion-oriented recommendation instead of generic design advice.
Practical workflow and reading order
Use the skill in this order: establish app context, identify framework constraints, inspect the current paywall, diagnose the likely bottleneck, then propose a revised variant and test plan. If the repo includes supporting context such as app-marketing-context.md, read that before editing the paywall because audience and price sensitivity affect the recommendation.
paywall-optimization skill FAQ
Is this only for paywall copy?
No. The paywall-optimization skill covers the full paywall conversion problem: offer structure, price framing, trial messaging, plan hierarchy, placement, and test design. Copy matters, but it is usually not the only lever.
Do I need to be using RevenueCat or Superwall?
No, but the skill is especially relevant if you are. It also applies to native StoreKit flows and other subscription setups, as long as you can describe the current offer and measure funnel results.
When should I not use this skill?
Do not use paywall-optimization if the real question is pricing strategy across the whole business, lifecycle retention after purchase, or where onboarding should trigger the paywall. Those are different decisions and will produce better results with a more specific skill or prompt.
Is it beginner-friendly?
Yes, if you can share screenshots and basic funnel data. You do not need deep conversion expertise to use the paywall-optimization guide effectively, but you do need to be specific about what you want improved and what is already known.
How to Improve paywall-optimization skill
Improve the quality of the input
The best way to get better results from paywall-optimization is to include the current paywall, the app category, the target audience, and the exact conversion gap you want to close. A vague request creates vague advice; a structured request lets the skill focus on the right bottleneck.
Share the constraints that affect the decision
Include any hard limits such as required legal copy, App Store policy concerns, pricing ceilings, localization needs, or engineering constraints. These details change whether the skill recommends a simple messaging tweak, a layout change, or a larger experiment.
Ask for testable output
Ask for a revised paywall variant, the reason each change should help conversion, and the one metric you will use to judge success. The strongest paywall-optimization results come from outputs that can be shipped and measured, not just reviewed visually.
Iterate using the first result
After the first pass, compare the suggested variant against actual funnel data and user behavior. If the paywall still underperforms, refine the input with screenshots, segment data, or alternate variants so the next pass can isolate whether the issue is offer fit, trust, or presentation.