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app-paywall-pilot Nikolai-Iakubovskii/app-paywall-pilot

A framework for designing App Store-compliant subscription paywalls. 4 layers: AI skill (Claude/GPT/Cursor) + knowledge base (79 sourced benchmarks) + Python LTV tool + docs. 20-concept academic foundation (Kahneman + Layer 2). Flagship domain Paywall; expansion planned to Onboarding, Retention, Growth, Pricing, Reviews.

HEALTH 69 / Stable
// hidden gem adaptyandroidapp-storeclaude-codeclaude-code-skillcroflutterin-app-purchaseiosmobile-monetization

// readme

πŸ“± Paywall Pilot Framework

A framework for designing App Store-compliant
subscription paywalls that convert.

πŸš€ Get Started β€’ πŸ—οΈ Architecture β€’ 🧩 Modules β€’ πŸ’‘ Use Cases β€’ πŸ“Š What You Get β€’ πŸ—ΊοΈ Roadmap β€’ 🀝 Contributing


πŸ—οΈ Architecture

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.

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚  πŸ€– SKILL LAYER          SKILL.md + modules/*.md            β”‚
β”‚  Entry point for AI assistants (Claude / GPT / Cursor)      β”‚
β”‚  Thin core + lazy modules + runtime contracts…
↑ The Undervalued Score +

How much a project earns versus how much attention it actually gets. Above 50 means the work is outrunning its audience. Recomputed nightly from commit velocity, contributor effort, issue resolution, fork utility, release cadence, and project maturity β€” divided by a logarithmic reach factor.

score  = signal / reach

signal = 0.25Β·commit_velocity   // commits in last 90 days (cap 30)
       + 0.20Β·contributor_work  // unique authors Γ— velocity (cap 100)
       + 0.20Β·issue_resolution  // closed Γ· total issues
       + 0.20Β·fork_ratio        // forks Γ· stars (proxy for real usage)
       + 0.10Β·release_cadence   // releases in 90 days (cap 3)
       + age_bonus              // +0 to +0.30 after 6 months
       + homepage_bonus         // +0.05 if homepage is set

reach  = log₁₀(stars + watchers + 10)β–ˆ
β™₯ The Health Score +

Is the project alive and maintained right now? A 0–100 pulse recomputed nightly from commit recency, rhythm, how fast issues close, and how quickly PRs get merged.

health = 0.35Β·recency       // days since last commit (90d decay)
       + 0.25Β·cadence       // commit rhythm consistency
       + 0.20Β·issue_health  // closed Γ· total issues
       + 0.20Β·pr_health     // merged Γ· total PRsβ–ˆ
β—· Health bands +

The colour and label on every card come straight from the health score.

Healthy   80 – 100   active, responsive, regular releases
Stable    60 – 79    maintained, steady, no alarms
Quiet     40 – 59    slowing down β€” watch this one
At Risk    0 – 39    going dark Β· candidate for rescueβ–ˆ
// Tags β€” what each label means +

Tags are independent behavioral signals computed nightly. A project can hold multiple at once. They drive the home page sections.

solo_builder      one person holds > 80% of commits (last 180d)
needs_contributors has open "help wanted" or "good first issue" labels
hidden_gem        < 100 stars Β· active in last 3 months Β· documented
legacy_hero       repo > 5 years old Β· committed this year
fork_magnet       forks/stars > 0.5 Β· used as template or dependency
release_machine   5+ releases in the last 90 days
under_pressure    > 10 open issues Β· ≀ 2 contributors Β· health β‰₯ 60
community_watch   watchers > stars Β· devs tracking before the public
community_hub     GitHub Discussions enabled Β· > 20 discussions
funded            maintainer has active funding channelβ–ˆ
β˜… Why rank against stars at all? +

Stars are an outcome, not effort. A project with 8 stars and daily commits is doing more interesting work than one coasting on 8k. We measure the building, then divide by the attention already received β€” so the genuinely undervalued rise to the top.

// stars   = lagging indicator
// commits = leading indicator
// we rank by the leading oneβ–ˆ