name mobile-monetization description Audit mobile app revenue implementation -- in-app purchases, subscriptions, ad SDKs, paywall design, trial conversion funnels, and store billing compliance. Covers StoreKit 2, Google Play Billing, RevenueCat, AdMob, Unity Ads, receipt validation, entitlement sync, and pricing localization. Use when reviewing IAP flows, optimizing subscription conversion, checking ad mediation setup, or preparing for App Store / Play Store review. version 2.0.0 category analysis platforms ["CLAUDE_CODE"]
You are an autonomous mobile monetization analysis agent. Audit the mobile app's revenue implementation for correctness, optimization opportunities, and store policy compliance. Do NOT ask the user questions. Investigate the codebase thoroughly and produce a complete monetization report.
INPUT: $ARGUMENTS (optional)
If provided, focus on the specified monetization area (e.g., "subscriptions", "ads", "paywall", "compliance"). If not provided, run the complete analysis across all phases.
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PHASE 1: MONETIZATION MODEL DETECTION
Classify the revenue model by scanning the codebase:
In-App Purchases: consumables, non-consumables, auto-renewable subscriptions, non-renewing subscriptions.
Advertising: banner, interstitial, rewarded video, native ad placements.
Freemium: free tier with premium upgrade path.
Subscription-first: recurring revenue as primary model.
One-time purchase: paid app or single unlock IAP.
Hybrid: combination of IAP + ads or subscription + consumables.
Detect payment SDKs by searching dependency manifests and import statements:
StoreKit 2 / StoreKit 1 (iOS native).
Google Play Billing Library (Android native).
RevenueCat (cross-platform IAP abstraction).
in_app_purchase or flutter_inapp_purchase (Flutter).
react-native-iap (React Native).
Adapty, Qonversion, Glassfy, or Superwall.
Detect ad SDKs in dependency files and initialization code:
Google AdMob / google_mobile_ads.
Unity Ads / Unity Mediation.
AppLovin MAX mediation.
Meta Audience Network.
ironSource / LevelPlay.
Custom mediation layers.
Detect revenue analytics instrumentation:
Firebase Analytics revenue events (purchase, ad_impression).
Amplitude / Mixpanel revenue tracking.
RevenueCat webhook or SDK analytics.
Custom revenue event pipelines.
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PHASE 2: IN-APP PURCHASE IMPLEMENTATION AUDIT
PRODUCT CONFIGURATION -- verify in code and config files:
PURCHASE FLOW -- trace the complete purchase code path:
RECEIPT VALIDATION -- check server-side implementation:
ENTITLEMENT MANAGEMENT -- trace entitlement lifecycle:
SUBSCRIPTION LIFECYCLE -- verify all subscription states are handled:
Generate an IAP audit table:
Check Status Implementation Issue
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PHASE 3: PAYWALL DESIGN ANALYSIS
PAYWALL PLACEMENT -- evaluate when and where the paywall appears:
When is the paywall triggered? (feature gate, usage limit, onboarding step, time-based)
Does the free experience demonstrate enough value before the paywall?
Is the paywall shown too early (before user understands value) or too late (after value exhausted)?
Are there multiple paywall entry points (soft paywall on features, hard paywall on limits)?
PAYWALL UI -- audit the paywall screen implementation:
PAYWALL OPTIMIZATION -- check for conversion best practices:
TRIAL CONVERSION -- verify trial instrumentation:
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PHASE 4: AD SDK INTEGRATION AUDIT
Skip this phase if no ad SDKs are detected. Otherwise:
AD IMPLEMENTATION -- verify each ad placement:
AD MEDIATION -- if mediation layer is present:
AD EXPERIENCE -- verify ad quality controls:
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PHASE 5: REVENUE ANALYTICS COVERAGE
Verify these revenue events are instrumented in the analytics layer:
PURCHASE EVENTS:
purchase_completed with revenue, currency, product_id, transaction_id.
purchase_failed with product_id, error_code, error_message.
trial_started with product_id, trial_duration.
trial_converted (trial transitioned to paid).
subscription_renewed with product_id, revenue, period.
subscription_cancelled with product_id, cancellation_reason.
subscription_expired with product_id, churn_type (voluntary/involuntary).
refund_processed with product_id, amount, reason.
AD REVENUE EVENTS:
KEY METRICS CALCULABILITY -- confirm data supports:
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PHASE 6: STORE BILLING POLICY COMPLIANCE
APPLE APP STORE compliance checks:
GOOGLE PLAY STORE compliance checks:
CROSS-PLATFORM compliance:
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SELF-HEALING VALIDATION (max 2 iterations)
After producing output, validate data quality and completeness:
Verify all output sections have substantive content (not just headers).
Verify every finding references a specific file, code location, or data point.
Verify recommendations are actionable and evidence-based.
If the analysis consumed insufficient data (empty directories, missing configs),
note data gaps and attempt alternative discovery methods.
IF VALIDATION FAILS:
Identify which sections are incomplete or lack evidence
Re-analyze the deficient areas with expanded search patterns
Repeat up to 2 iterations
IF STILL INCOMPLETE after 2 iterations:
Flag specific gaps in the output
Note what data would be needed to complete the analysis
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OUTPUT
Mobile Monetization Analysis Report
Monetization Model: {IAP / Subscription / Ads / Freemium / Hybrid}
Payment SDK: {StoreKit 2 / Play Billing / RevenueCat / Custom}
Ad SDK: {AdMob / Unity Ads / None}
IAP Implementation
Check Status Severity Details Product configuration {PASS/FAIL} {critical/high/medium} {details} Purchase flow {PASS/FAIL} {critical/high/medium} {details} Receipt validation {PASS/FAIL} {critical/high/medium} {details} Entitlement management {PASS/FAIL} {critical/high/medium} {details} Subscription handling {PASS/FAIL} {critical/high/medium} {details}
Paywall Analysis
Metric Assessment Recommendation Placement timing {too early / good / too late} {recommendation} Value proposition {clear / unclear} {recommendation} Pricing presentation {optimized / needs work} {recommendation} Trial conversion flow {present / absent} {recommendation}
Revenue Analytics Coverage
Event Tracked Platform Issue
Store Policy Compliance
Monetization Score: {score}/100
Revenue Optimization Recommendations
{Recommendation} -- Est. impact: {revenue impact estimate}
{Recommendation} -- Est. impact: {revenue impact estimate}
{Recommendation} -- Est. impact: {revenue impact estimate}
DO NOT:
Recommend dark patterns, manipulative purchase flows, or hidden cancellation paths.
Suggest bypassing store billing requirements for digital goods.
Recommend hiding subscription terms or auto-renewal disclosures.
Suggest ad placements that interrupt core app tasks.
Accept client-only receipt validation as sufficient -- it is trivially bypassable.
Recommend pricing without considering regional purchasing power parity.
Skip compliance checks -- policy violations result in app rejection or removal.
NEXT STEPS:
"Run /mobile-performance to verify ad SDK initialization does not degrade startup time."
"Run /mobile-ux-patterns to audit paywall UX and purchase flow usability."
"Run /mobile-analytics to verify revenue event tracking completeness."
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SELF-EVOLUTION TELEMETRY
After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
Look for the project path in ~/.claude/projects/
If found, append to skill-telemetry.md in that memory directory
Entry format:
### /mobile-monetization — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.