paywall-optimization

paywall-optimization is a skill for Claude Code from Eronred/aso-skills. It costs 182 tokens per session (1,839 once invoked), scanned A, original, MIT.

Guidance for designing and testing an app’s paywall, the screen that asks users to subscribe or pay before using restricted features.

In plain words
What is it for?
Use it to review paywall placement, plan structures, trial offers, subscription metrics, and A/B test ideas across RevenueCat, Superwall, Adapty, or native StoreKit setups.
Why use it?
It provides a structured way to diagnose where people leave the subscription process before changing the layout, wording, prices, or offer.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: positional $N argument.

Part of the aso-skills plugin — 40 skills shipped together

Good fit Use it to review paywall placement, plan structures, trial offers, subscription metrics, and A/B test ideas across RevenueCat, Superwall, Adapty, or native StoreKit setups.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/eronred/aso-skills/paywall-optimization
About the project

ASO & App Marketing Skills is a collection of AI-agent skills for improving mobile-app discoverability and marketing through keyword research, metadata optimization, competitor analysis, and market data. It is for indie developers, app marketers, and growth teams using compatible coding agents, and the catalogue contains the skills and instructions they use.

Eronred/aso-skills · 1,843 stars · on GitHub · appeeky.com

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Eronred/aso-skills --skill paywall-optimization
Clone the repo
git clone --depth 1 https://github.com/Eronred/aso-skills

Made for: Claude Code.

Or install aso-skills, the plugin that ships this one along with the rest of its 40 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for paywall-optimization

README.md
[![agentmods](https://agentmods.dev/badge/skills/eronred/aso-skills/paywall-optimization/github.svg)](https://agentmods.dev/skills/eronred/aso-skills/paywall-optimization)
Your own site
<a href="https://agentmods.dev/skills/eronred/aso-skills/paywall-optimization"><img src="https://agentmods.dev/badge/skills/eronred/aso-skills/paywall-optimization/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for paywall-optimization

Your own site · 80×15
<a href="https://agentmods.dev/skills/eronred/aso-skills/paywall-optimization"><img src="https://agentmods.dev/badge/skills/eronred/aso-skills/paywall-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 182 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,839 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 8 May 2026
  • Snyk pass 8 May 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00182 $0.01839
Opus 5 $0.00091 $0.00920
Sonnet 5 $0.00036 $0.00368
Haiku 4.5 $0.00018 $0.00184

Measured 10d ago against content hash 3e391c6f14bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

paywall-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/paywall-optimization/SKILL.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 144 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 144 lines · 182 tokens per session scan A 3e391c6f14bb

Subscribe to this mod's changes

paywall-optimization is a skill published in the GitHub repository Eronred/aso-skills (1,843 stars, last pushed 18d ago), licensed MIT. It adds 182 tokens to every session and 1,839 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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