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A framework for designing App Store-compliant subscription paywalls. 4 layers: AI skill (Claude/GPT/Cursor) + knowledge
AI skill + knowledge base + executable tool. For iOS + Android. Built on 88 sourced 2026 benchmarks and platform changes (Adapty 16K apps · RevenueCat 115K apps · AppsFlyer 1.7B installs · Superwall 32M paywall views · Apple/Google store updates) and a 20-concept academic foundation (Kahneman + Layer 2). Flagship domain is Paywall; expansion to Onboarding / Retention / Growth / Pricing on the roadmap.
🚀 Get Started • 🏗️ Architecture • 🧩 Modules • 💡 Use Cases • 📊 What You Get • 🗺️ Roadmap • 🤝 Contributing
Paywall Pilot is not just a Claude Code skill (though it works as one). It's a 4-layer framework, designed to be used whole or per-layer.
Runtime behavior is intentionally smaller than this README: agents should start from SKILL.md, then use runtime/ for input contracts, routing, and source lookup rules.
┌─────────────────────────────────────────────────────────────┐
│ 🤖 SKILL LAYER SKILL.md + modules/*.md │
│ Entry point for AI assistants (Claude / GPT / Cursor) │
│ Thin core + lazy modules + runtime contracts │
├─────────────────────────────────────────────────────────────┤
│ 📚 KNOWLEDGE LAYER sources.json + outputs/ │
│ Every numeric claim sourced. Research briefs. │
│ Use standalone or referenced by Skill layer. │
├─────────────────────────────────────────────────────────────┤
│ 🔧 TOOL LAYER tools/ltv-calculator.py │
│ Executable utilities. CLI + JSON I/O. │
│ Callable by Skill layer OR from Bash. │
├─────────────────────────────────────────────────────────────┤
│ 📘 REFERENCE LAYER docs/ + examples/ │
│ Human-readable playbooks, checklists, migrations, examples │
│ Works without any AI tool. │
└─────────────────────────────────────────────────────────────┘
| You want to… | Use layer |
|---|---|
| Get an AI paywall audit | Skill (install into Claude Code / paste into ChatGPT) |
| Look up a specific benchmark with source | Knowledge (sources.json) |
| Calculate LTV / ROAS / breakeven | Tool (python3 tools/ltv-calculator.py) |
| Read a printable pre-ship checklist | Reference (docs/audit-checklist.md) |
| Migrate from a banned pattern | Reference (docs/migrations/) |
| See a worked audit example | Reference (examples/) |
Concrete jobs-to-be-done. Match your situation; the framework activates the relevant modules automatically.
| 👤 Your situation | 💬 Ask your AI… | 🧩 What activates |
|---|---|---|
| You're designing your first paywall. | "Help me design the paywall for my new fitness app." | Full audit · taxonomy · screen anatomy · copy library · category benchmarks |
| Apple just rejected you. | "I got rejected for Guideline 3.1.2. Help me fix it." | Compliance triage · toggle-paywall migration playbook · field-report decision tree |
| You don't know if your numbers are good. | "Install→trial 9%, trial→paid 22%, refund 6%. Are these OK?" | Indie Dev FAQ · category benchmarks · performance grading |
| You need to know if you'll be profitable. | "I have 40k installs, $3 CPI, annual $79.99 / monthly $9.99 (50/50). Will I break even?" | Unit Economics Calculator (Python + module) · ROAS projection · what-if scenarios |
| You don't know what to test next. | "I have 2 weeks before launch. What's the highest-leverage test?" | Decision-tree #7 (test priority) · Adapty 2026 win rates · sample-size minimums |
| You want to copy what big apps do. | "How does Calm/Noom/Cal AI/Tinder structure their paywall?" | Annotated big-app teardowns with sources |
| Your trial-to-paid is low. | "Trial-to-paid is 18% — why?" | Decision-tree #3 · Blinkist Trial Timeline pattern · notifications-lifecycle |
| You're expanding to new markets. | "I'm US-only at $79.99/yr. Where should I expand and at what price?" | Localization · pricing index US=1.0 · AppsFlyer 2026 emerging-markets data |
| You need better paywall copy. | "Write 3 headline variants for my running app paywall." | Copy library: 12 headline formulas · Copy Ladder · weak/risky words · locale notes |
| Your refund rate is high. | "Annual refund rate is 7% — what's wrong?" | Refund management · Apple Consumption API · channel-level diagnostic |
If your situation isn't on this list, just send a screenshot and say "Audit my paywall". You get a concise screenshot-first audit by default; ask for a deep audit when you want the full breakdown.
| 🎯 Output | 📦 What's inside | 🔧 Powered by |
|---|---|---|
| Pareto paywall audit (default) | Observed issues · top 3 fixes · missing context · next test · short sources | SKILL.md + runtime Pareto cards |
| Deep paywall audit (on request) | Current state · main problem · access model + placement + presentation · screen content · copy variants · localization notes · ranked test plan · iOS review risks · Android delta | SKILL.md deep output |
| Paywall design spec | Recommended model · screen spec · copy draft · compliance watchouts · first A/B test · short sources | SKILL.md design mode + runtime Pareto cards |
| Unit-economics projection | LTV / ARPU / ROAS / breakeven across 8 horizons (7d → 4yr) · performance grading vs benchmarks · top-3 actions · what-if scenarios | unit-economics-calculator.md + Python script |
| Direct answers to single questions | "Should I add a weekly plan?" → verdict + reason + one action. 35+ pre-answered scenarios. | indie-dev-faq.md |
| Big-app pattern reference | Annotated teardowns with structure / copy / pricing / takeaway / source | teardowns.md |
| Compliance triage | Apple Rule vs Field Report distinction · 7-level evidence ladder · printable 50+ item checklist | SKILL.md + audit-checklist.md |
| Migration playbooks | Step-by-step migration from banned patterns (trial-toggle paywall today; future bans tomorrow) | docs/migrations/ |
Most paywall advice is one of three things, all of them broken in 2026:
And most AI assistants compound the problem: they recommend whatever pattern they saw most often in 2023 training data, with no source, no compliance check, and no awareness of category economics.
Paywall Pilot Framework grounds every recommendation in published evidence with confidence labels, never invents data, and explicitly states when to ignore benchmarks (e.g., when N<1,000 subs/variant or when your category is niche).
| Principle | How |
|---|---|
| 🛡️ Compliance first | Every recommendation checked against Apple App Store Review Guidelines. Apple Rule > vendor data, always. |
| 📊 7-level evidence ladder | Every claim labeled from Apple Rule down to Hypothesis — no unmarked assertions. |
| 🔗 Sourced data | Every benchmark carries source + date + sample size. Canonical manifest lives in sources.json and is validated locally. |
| 🏛️ Big-app teardowns | 11 annotated paywall analyses (Calm, Duolingo, Noom, Cal AI, Tinder, Strava, Headspace, Blinkist, Flo, ChatGPT, AI companions). |
| 🎓 Academic foundations | Pricing psychology grounded in Tversky-Kahneman 1981 (Science), Anderson-Simester 2003, Thomas-Morwitz 2005, Ariely 2008, Cialdini — not just blog posts. |
| ♻️ Full lifecycle | 10 stages from first purchase through win-back. Notifications, refunds, cohorts. |
| 🧩 Modular | Core SKILL.md plus deep-dive modules loaded on demand by topic. |
| 🧠 Common-sense overrides | Explicit "WHEN TO IGNORE BENCHMARKS" guidance for small N, niche category, recent launch. |
| 🤖 LLM-agnostic | Works with Claude Code, Codex, ChatGPT, Cursor, Windsurf, Gemini — any AI tool with custom instructions. |
The Skill layer is split into a core SKILL.md plus deep-dive modules under modules/, an executable Python calculator, worked examples, and migration playbooks. Load on demand based on the task.
| Module | What's inside |
|---|---|
| copy-library.md | The Copy Ladder (Outcome > Benefit > Feature), 12 headline formulas, benefit-bullet patterns, CTA templates with action+benefit pattern, weak/risky words list, length expansion factors and formality cheatsheet for 12+ locales, loading-screen copy, pricing-block templates |
| teardowns.md | Annotated paywall breakdowns for Calm, Duolingo, Noom, Cal AI, Tinder, Strava, Headspace, Blinkist, Flo, ChatGPT, AI companions -- structure, copy, pricing, takeaway, source for each |
| pricing-psychology.md | Tversky & Kahneman 1981 (framing, Science), Anderson & Simester 2003 ($9 endings, field experiment), Thomas & Morwitz 2005 (left-digit), Ariely 2008 (decoy with replication caveats), Cialdini 7 principles + 2024 Springer mobile-app strength data, anchor / decoy / charm / per-day / PPP frameworks, Hollow Middle 2026 trend, Apple monthly subscriptions with 12-month commitment, Apple SBP explainer |
| decision-trees.md | 10 diagnostic flowcharts: access model choice, low-conversion triage, plan architecture, surface choice, when-to-test priority, compliance triage, refund diagnosis, vendor-data conflict resolution |
| category-deep-dives.md | Per-category economics for Health & Fitness, Gaming, Productivity, Lifestyle, Education, AI, Photo & Video, Travel, Shopping, B2B; geography cuts NA / Western Europe / IN-SEA |
| screen-anatomy.md | Visual hierarchy, F-pattern layout, thumb zone, spacing rhythm, pricing block anatomy, accessibility (WCAG AA, Dynamic Type, VoiceOver), dark mode, safe areas, loading and error states |
| localization.md | Adapty 2026 4.4x pricing variance data, App Store auto-tier vs manual per-territory vs geo-tier strategy with cost/benefit, Apple 2026 metadata languages, copy length expansion factors, formality, RTL, number formatting, cultural trust signals |
| android-parity.md | Play Billing Library v9 concepts (Base Plan + Offers), EU DMA alternative billing, Android refund reality (RC: 31% involuntary failures vs iOS 14%), AppsFlyer 2026 Android growth (4x faster than iOS), Play Age Signals watchouts |
| unit-economics-calculator.md | Conversational LTV / ARPU / ROAS / breakeven calculator with default retention multipliers, performance grading thresholds, expert advice engine with 11 conditional recommendations, scenario modeling, full worked example |
| indie-dev-faq.md | 35+ direct-answer Q&A: "Should I add weekly?", "Are my numbers good?", "Why is trial-to-paid low?", "What's a healthy LTV:CAC?" -- threshold + verdict + one action with cited source |
| cac-acquisition.md | CAC formula and variants, 2026 mobile CAC benchmarks (iOS / Android, premium and emerging markets), channel CPI table (ASA / Meta / TikTok / Google / Snap / Reddit), LTV:CAC thresholds, channel mix strategy by stage, MMP comparison (AppsFlyer / Adjust / Singular / RevenueCat), Apple Search Ads tactics, Web2App economics, common CAC mistakes |
| onboarding-paywall-handoff.md | Continuity principle made concrete. 7 onboarding patterns linked to paywall (Noom quiz, Cal AI demo, Headspace segmented, Duolingo goal-first, Strava aha, Flo empathy, reverse trial). Loading-screen bridge templates. Decision rule for onboarding length by LTV. |
| notifications-lifecycle.md | Push + email sequences for trial (Blinkist Day-5 +1,200% opt-in), abandon recovery (Superwall 17% revenue), renewal-risk, billing-issue, win-back. Permission strategy, copy templates, tooling choice. |
| glossary.md | Canonical definitions: ARPU vs ARPPU, gross vs RLTV, CR variants, MRR/ARR, CAC variants (CPI/CPR/CAC/eCAC), ROAS, retention vs renewal vs churn. Plus 24-acronym quick reference. |
| refund-management.md | Refund baselines per plan + region, prevention sequence (in-app + push), Apple Consumption API for refund decline (Swift example), Subscription Pause alternative, channel-level diagnostic flowchart. |
| cohort-analysis.md | Three cohort types (install / trial / calendar), how to read App Store Connect Analytics + RC/Adapty/Apphud dashboards, 7 common cohort mistakes, healthy retention curve patterns, pre/post-change comparison setup. |
Plus standalone documentation, executable tools, worked examples, and research:
Every recommendation carries a confidence label. Higher = more reliable.
| Level | Label | Meaning |
|---|---|---|
| 1 | Apple Rule | Published in App Store Review Guidelines |
| 2 | Apple Guidance | Apple documentation, WWDC sessions, or HIG recommendations not enforced as rejection criteria |
| 3 | Platform Capability | Feature documented in StoreKit, App Store Connect, or Google Play Billing |
| 4 | Vendor Aggregate Data | Large-scale report from SDK provider (10K+ apps or $1B+ tracked) |
| 5 | Vendor Case Study | Published result from a single app or small group |
| 6 | Operator Insight | Practitioner observation without published dataset |
| 7 | Hypothesis | Directional signal, must be validated on this app |
Plus two academic-foundation classes used in pricing-psychology.md: academic (peer-reviewed papers) and platform_capability (documented features).
The paywall system has four independent axes. Separate them when analyzing or recommending.
Axis 1: Access Model -- What we sell (8 types)
| Model | Evidence |
|---|---|
| Hard paywall | Vendor Aggregate Data |
| Freemium / soft paywall | Apple Guidance |
| Metered paywall | Apple Guidance |
| Reverse trial | Operator Insight |
| Hybrid (subscription + one-time) | Operator Insight |
| Multi-tier | Platform Capability |
| Credits / usage packs | Apple Guidance |
| Family Sharing | Platform Capability |
Axis 2: Placement -- When we show it (13 types)
Onboarding, Post-aha, Feature gate, Usage limit, Post-close offer, Transaction abandon, Session-start, Renewal-risk, Billing issue / grace period, Win-back, Push marketing, Plan-upgrade, Value primer / bridge.
Axis 3: Presentation Pattern -- How we show it (9 types)
One-screen paywall, Value stack, Social-proof-led, Comparison table, Trial timeline, Video/demo paywall, Long-form landing, Interactive/gamified, Contextual mini-paywall.
Axis 4: Surface / Rendering Layer -- Where it renders (6 types)
Native StoreKit sheet, Provider remote paywall (Adapty/RevenueCat), Custom native screen, WebView hybrid, System-provided sheet (grace period, price increase consent), Push / notification deep-link surface.
Different categories have different economics. Do not apply one playbook to all apps. Full per-category deep-dives in modules/category-deep-dives.md.
| Category | Dominant Plan | Trial Impact | Notable 2026 stat |
|---|---|---|---|
| Health & Fitness | 60.6% annual | Trial helps (35-42% trial-to-paid) | Install LTV $1.21 (highest of any category) |
| Gaming | 82% weekly | Short trial works | 73% use ≤4-day trials |
| Productivity | 77% monthly | Trial hurts LTV ($56.95 direct vs $49.13 trial) | Direct buyers preferred |
| Lifestyle | Mixed | Direct buyers ~21% more valuable at 12mo | Similar to Productivity |
| Education | Annual / mixed | Trial users +50.4% 12-mo LTV | Highest discount usage (14.3%) |
| AI Apps | 59.8% monthly | Higher pricing tolerated, churn 30% faster | $20/mo (ChatGPT) is the baseline |
| Photo & Video | Mixed | Trial-to-paid 22.2% (lowest) | APAC refund rate 14.1% |
| Travel | 3+ plans | Trial-to-paid 43.5% (highest) | Annual median $20 (lowest) |
| Shopping | 1-plan dominant | Mixed monetization | 40% use single-plan paywalls |
| Business / B2B | Annual | Trial filters intent | $35.48 12-mo RLTV per payer |
Per AppsFlyer 2026 (1.7B installs, 2,900 apps): subscription UA spend grew 24% YoY but the growth is not where you might think.
| Region | D30 Trial | D35 Paid | D14 RPI | 12-mo RLTV per payer |
|---|---|---|---|---|
| North America | 7.1% | 2.8% | $0.38 | $32 |
| Western Europe | mid | mid | mid | $25 |
| IN/SEA | 3.0-3.7% | 0.7% | $0.08 | $14 |
Strategic implication: NA-only apps face flat user pool; emerging markets are where Android growth happens. Geo-tier pricing (ChatGPT Go @ $8 for India) is increasingly important.
Discovery & Audit --> Paywall Strategy --> Screen Design
| |
v v
iOS Compliance <-- Implementation <-- Testing Plan
| Stage | Action |
|---|---|
| First purchase | Main paywall |
| Post-close recovery | Discounted offer (banner) |
| Transaction abandon | Soft prompt with alternative |
| Trial-to-paid | Reminder / value reinforcement |
| Renewal risk | Promotional offer to retain |
| Billing issue | Grace period UX + retry |
| Price increase | Consent flow |
| Win-back | Win-back offer (iOS 18+) |
| Plan upgrade | Upsell to higher tier |
| Refund / support | Support surface, consumption data |
| Template | When to use |
|---|---|
| Post-Onboarding | Hard paywall after onboarding quiz |
| Feature Gate | User taps a locked premium feature |
| Usage Limit | Free tier cap reached |
| Transaction Abandon | User cancelled payment sheet |
| Post-Close Offer | Dismissed main paywall -- show banner |
| Win-Back | Lapsed subscriber returns |
Plus: Metered Paywall, Reverse Trial, One-Time Unlock, Renewal-Risk / Churn-Save, Intent-Tiered Paywall.
Annotated breakdowns of 11 high-revenue subscription apps in modules/teardowns.md. One-line summary:
| App | Single biggest pattern |
|---|---|
| Calm | Single plan (no tier choice) -- universal value = no choice paralysis |
| Duolingo | Brand-consistent paywall + "Start my free week" CTA pattern |
| Noom | 77-step onboarding -> "your reserved plan" -> paywall |
| Cal AI | Hard paywall + obsessive personalization + single core action ($1.4M/mo profit) |
| Tinder | Blur-to-reveal Zeigarnik tension + ML-driven dynamic paywall |
| Strava | 30-day trial without credit card (ultimate trust signal) |
| Headspace | Equal-prominence monthly + annual (NOT annual-default) + day/night theming |
| Blinkist | Trial Timeline visual: +23% trial signups, -55% complaints, +1,200% notif opt-in |
| Flo | 70-screen onboarding + free core / premium upsell ($6M/mo revenue) |
| ChatGPT | $20/mo became AI baseline; geo-tier (Go @ $8) for emerging markets |
| AI companions | High price tolerance + high churn -- optimize first-month conversion |
| Metric | Value | Source |
|---|---|---|
| Install to Trial (global) | 10.9% | Adapty |
| Install to Trial (North America) | 14.5% | Adapty |
| Trial to Paid (global) | 25.6% | Adapty |
| Trial to Paid (Health & Fitness) | 35.0-42.2% | Adapty/RC |
| Trial to Paid (Travel) | 43.5% (highest) | RC |
| Trial to Paid (Photo & Video) | 22.2% (lowest) | RC |
| Hard paywall D35 conversion | 10.7% | RevenueCat |
| Freemium D35 conversion | 2.1% | RevenueCat |
| Hard paywall vs freemium | ~5x | Both |
| Day 0 share of trial starts | ~80-90% | Both |
| Hard paywall LTV uplift vs soft | +21% per subscriber | Adapty |
| Experiment Type | LTV Win Rate |
|---|---|
| Localization | 62.3% |
| Trial structure | 59.6% |
| Plan duration | 58.7% |
| Number of plans | 57.1% |
| Price changes | 45.5% |
| Visual/copy | 34.6% |
Key insight: Structure tests beat copy tests by ~2x. Don't start with polish. Localize first -- highest win rate of any test category.
| Period | Median | Range |
|---|---|---|
| Weekly | $5.99-$7.48 | $4.99-$6.99 |
| Monthly | $10.00-$12.99 | $7.99-$9.99 |
| Annual | $34.80-$38.42 | $29.99-$39.99 |
EU charges 29-39% more than NA. IN/SEA RLTV is ~46% of NA on annual.
| Trial length | Cancel Day 0 |
|---|---|
| 3 days | 55.4% |
| 7 days | 39.8% |
Short trials = aggressive (more conversions, more refunds). 7-day is the balanced default.
| Metric | Value |
|---|---|
| Google Play involuntary billing failures | ~31% of cancellations |
| App Store involuntary billing failures | 14% of cancellations |
| Photo & Video APAC refund rate | 14.1% (highest regional) |
Trial length impact, Revenue per install (D14 + D60), Plan architecture by category, Revenue share by plan type, 12-month retention by plan, RLTV per payer by category / region / price tier, Superwall aggregate data (32M views, 18-company abandon study), AppsFlyer 2026 (1.7B installs, Android 4x growth), Apple Analytics benchmarks.
| Pattern | Evidence Level | Source |
|---|---|---|
| Trial Timeline ("Honest Paywall") | Vendor Case Study -- single company (+23% signups, -55% complaints, +1,200% notif opt-in) | Blinkist via Purchasely |
| Personalized Headline from Quiz | Vendor Aggregate Data -- 15%+ lift, methodology not open | Adapty 2026 report |
| Animated Paywall | Vendor Aggregate Data -- 2.9x vs static, methodology not open | Adapty 2026 report |
| 3 Products vs 1 | Aggregate Study -- +61% (1->2) and +44% (2->3), 32M views, 15 apps | Superwall |
| Transaction Abandon Recovery | Aggregate Study -- 17% of total revenue, 18 companies | Superwall |
| "Building Your Plan" loader | Operator Insight -- no published data, widely adopted | Industry observation |
| Long Onboarding Quiz | Operator Insight -- consistent in highest-revenue H&F apps | Noom, Flo, Cal AI |
| Multi-Page Paywall | Hypothesis -- conflicting results (Speak4Me +27% vs Stompers single-page win) | Superwall |
| "Design Your Trial" | Hypothesis -- methodology unpublished | Superwall |
Mobile paywall pricing isn't invented in vendor blogs. The foundational research:
Full mobile-application mapping in modules/pricing-psychology.md.
Quickest start (clone the whole skill including modules):
git clone https://github.com/Nikolai-Iakubovskii/app-paywall-pilot \
~/.claude/skills/app-paywall-pilot
Then run /app-paywall-pilot in your project. The core SKILL.md loads first; modules load on demand when relevant.
Single-file install (core only):
mkdir -p ~/.claude/skills/app-paywall-pilot
curl -o ~/.claude/skills/app-paywall-pilot/SKILL.md \
https://raw.githubusercontent.com/Nikolai-Iakubovskii/app-paywall-pilot/main/SKILL.md
Add SKILL.md content to your project's AGENTS.md or custom instructions. Reference modules via raw GitHub URLs.
Paste SKILL.md as a system prompt, then ask: "Audit my paywall" with a screenshot. For deeper analysis, also paste the relevant module (e.g. teardowns.md when comparing to Calm).
Add SKILL.md to your project root or AI rules config. Modules in modules/ will be findable in workspace.
Read SKILL.md and the modules directly -- they're a structured knowledge base that works without any AI tool. Designed to be human-readable.
Ask your AI: "Audit my paywall" with a screenshot. The default output is 12 sections:
1. Current state -- app context, placement, plan config
2. Main problem -- plain language
3. Recommended access model -- from taxonomy
4. Recommended placements -- from taxonomy
5. Recommended presentation -- from taxonomy
6. Screen content -- headline, benefits, pricing, CTA, trust
7. Copy variants -- 2-3 short and long versions, Copy Ladder rung
8. Layout sketch -- ASCII or block diagram, thumb-zone confirmed
9. Localization notes -- pricing per market, CTA length budget per locale
10. Tests to run -- ranked by Adapty 2026 win rate (localization first)
11. iOS review risks -- Apple Rules + Field Reports
12. Android delta -- Play Billing differences if cross-platform
Each finding labeled with its evidence level.
| Source | Dataset | Date | Class |
|---|---|---|---|
| Adapty State of In-App Subscriptions 2026 | 16,000 apps, $3B revenue, 500M txns, 105K paywalls, 50+ countries | 2026-03-14 | large_scale_report |
| Adapty Health & Fitness Benchmarks 2026 | Category cut from $3B dataset | 2026 | large_scale_report |
| Adapty Paywall Experiments Playbook | Adapty platform A/B tests | 2026 | vendor_blog |
| RevenueCat State of Subscription Apps 2026 | 115,000+ apps, $16B+ revenue, 1B+ txns | 2026-03 | large_scale_report |
| Superwall Product Count Study | 32.3M paywall opens, 15 apps, 383K conversions | 2025 | aggregate_study |
| Superwall Transaction Abandon Study | 18 companies, 525K users | 2024-08 | aggregate_study |
| AppsFlyer State of Subscriptions 2026 | 1.7B paid installs, 2,900 apps, $2.1B UA spend | 2026 | large_scale_report |
| Apphud Subscription Guide | Apphud platform (no public sample size) | 2025 | vendor_blog |
| RevenueFlo iOS Rejections | Developer rejection reports | 2026 | field_observation |
| Apple App Store Review Guidelines | Official | Current | apple_docs |
| Apple Analytics | App Store Connect benchmarks | Current | apple_docs |
| Tversky & Kahneman 1981 | Science, 17K+ citations | 1981 | academic |
| Anderson & Simester 2003 | Quantitative Marketing and Economics field experiment | 2003 | academic |
| Thomas & Morwitz 2005 | Journal of Consumer Research | 2005 | academic |
| Ariely Decoy Effect | Predictably Irrational, Ch. 1 | 2008 | academic |
| Springer 2024 Mobile Persuasion | Cialdini principles in mobile contexts | 2024 | academic |
Full source manifest with every numeric claim: sources.json (50+ entries with URL, date, scope, evidence class).
Independent paywall screenshot libraries used for teardowns: Adapty Paywall Library • Mobbin • ScreensDesign • Paywallscreens.com • NamiML • Growth.Design • Funnel Teardowns
Paywall is the flagship domain. Framework will expand into adjacent subscription-app domains. Full detail in ROADMAP.md.
| Priority | Domain | Status | Release |
|---|---|---|---|
| — | Paywall | ✅ Production (v4.0, flagship domain) | Shipped |
| 1 | Onboarding (patterns, copy, teardowns, compliance, metrics) | 📋 Planned | v5.0 |
| 2 | Retention & Lifecycle (habit loops, streaks, churn prevention, dunning) | 📋 Planned | v6.0 |
| 3 | Growth & Acquisition (ASO, ASA, paid UA, web2app, attribution) | 📋 Planned | v7.0 |
| 4 | Pricing Strategy (as standalone domain; tier design, dynamic pricing) | 📋 Planned | v8.0 |
| 5 | Reviews & Reputation (prompt timing, recovery flows) | 📋 Planned | v9.0 |
The 20-concept academic foundation (Kahneman + Layer 2) already applies cross-domain — it's in the shared layer. Each new domain gets its own modules while inheriting the foundations.
Non-goals: generic web CRO, enterprise B2B sales, original academic research, competing with vendor SDKs. See ROADMAP.md for full scope.
See CHANGELOG.md for full release notes.
PRs welcome. Ground rules:
modules/ and reference from SKILL.md. Every line in the core costs tokens for every user.Indie developer shipping MistyWay • AuroraMe
Follow / DM / argue with me:
Open an issue or DM me on any of the above with feedback, missing benchmarks, or app teardown requests.
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