Mobile Design

Mobile Design System

Philosophy: Touch-first. Battery-conscious. Platform-respectful. Offline-capable. Core Principle: Mobile is NOT a small desktop. THINK mobile constraints, ASK platform choice.

🔧 Runtime Scripts

Execute these for validation (don't read, just run):

🔴 MANDATORY: Read Reference Files Before Working!

⛔ DO NOT start development until you read the relevant files:

Universal (Always Read)

🧠 mobile-design-thinking.md is PRIORITY! This file ensures AI thinks instead of using memorized patterns.

Platform-Specific (Read Based on Target)

🔴 If building for iOS → Read platform-ios.md FIRST! 🔴 If building for Android → Read platform-android.md FIRST! 🔴 If cross-platform → Read BOTH and apply conditional platform logic!

⚠️ CRITICAL: ASK BEFORE ASSUMING (MANDATORY)

STOP! If the user's request is open-ended, DO NOT default to your favorites.

You MUST Ask If Not Specified:

⛔ AI MOBILE ANTI-PATTERNS (YASAK LİSTESİ)

🚫 These are AI default tendencies that MUST be avoided!

Performance Sins

Touch/UX Sins

Security Sins

Architecture Sins

📱 Platform Decision Matrix

When to Unify vs Diverge

                    UNIFY (same on both)          DIVERGE (platform-specific)
                    ───────────────────           ──────────────────────────
Business Logic      ✅ Always                     -
Data Layer          ✅ Always                     -
Core Features       ✅ Always                     -
                    
Navigation          -                             ✅ iOS: edge swipe, Android: back button
Gestures            -                             ✅ Platform-native feel
Icons               -                             ✅ SF Symbols vs Material Icons
Date Pickers        -                             ✅ Native pickers feel right
Modals/Sheets       -                             ✅ iOS: bottom sheet vs Android: dialog
Typography          -                             ✅ SF Pro vs Roboto (or custom)
Error Dialogs       -                             ✅ Platform conventions for alerts

Quick Reference: Platform Defaults

🧠 Mobile UX Psychology (Quick Reference)

Fitts' Law for Touch

Desktop: Cursor is precise (1px)
Mobile:  Finger is imprecise (~7mm contact area)

→ Touch targets MUST be 44-48px minimum
→ Important actions in THUMB ZONE (bottom of screen)
→ Destructive actions AWAY from easy reach

Thumb Zone (One-Handed Usage)

┌─────────────────────────────┐
│      HARD TO REACH          │ ← Navigation, menu, back
│        (stretch)            │
├─────────────────────────────┤
│      OK TO REACH            │ ← Secondary actions
│       (natural)             │
├─────────────────────────────┤
│      EASY TO REACH          │ ← PRIMARY CTAs, tab bar
│    (thumb's natural arc)    │ ← Main content interaction
└─────────────────────────────┘
        [  HOME  ]

Mobile-Specific Cognitive Load

For deep dive: touch-psychology.md

⚡ Performance Principles (Quick Reference)

React Native Critical Rules

// ✅ CORRECT: Memoized renderItem + React.memo wrapper
const ListItem = React.memo(({ item }: { item: Item }) => (
  <View style={styles.item}>
    <Text>{item.title}</Text>
  </View>
));

const renderItem = useCallback(
  ({ item }: { item: Item }) => <ListItem item={item} />,
  []
);

// ✅ CORRECT: FlatList with all optimizations
<FlatList
  data={items}
  renderItem={renderItem}
  keyExtractor={(item) => item.id}  // Stable ID, NOT index
  getItemLayout={(data, index) => ({
    length: ITEM_HEIGHT,
    offset: ITEM_HEIGHT * index,
    index,
  })}
  removeClippedSubviews={true}
  maxToRenderPerBatch={10}
  windowSize={5}
/>

Flutter Critical Rules

// ✅ CORRECT: const constructors prevent rebuilds
class MyWidget extends StatelessWidget {
  const MyWidget({super.key}); // CONST!

  @override
  Widget build(BuildContext context) {
    return const Column( // CONST!
      children: [
        Text('Static content'),
        MyConstantWidget(),
      ],
    );
  }
}

// ✅ CORRECT: Targeted state with ValueListenableBuilder
ValueListenableBuilder<int>(
  valueListenable: counter,
  builder: (context, value, child) => Text('$value'),
  child: const ExpensiveWidget(), // Won't rebuild!
)

Animation Performance

GPU-accelerated (FAST):     CPU-bound (SLOW):
├── transform               ├── width, height
├── opacity                 ├── top, left, right, bottom
└── (use these ONLY)        ├── margin, padding
                            └── (AVOID animating these)

For complete guide: mobile-performance.md

📝 CHECKPOINT (MANDATORY Before Any Mobile Work)

Before writing ANY mobile code, you MUST complete this checkpoint:

🧠 CHECKPOINT:

Platform:   [ iOS / Android / Both ]
Framework:  [ React Native / Flutter / SwiftUI / Kotlin ]
Files Read: [ List the skill files you've read ]

3 Principles I Will Apply:
1. _______________
2. _______________
3. _______________

Anti-Patterns I Will Avoid:
1. _______________
2. _______________

Example:

🧠 CHECKPOINT:

Platform:   iOS + Android (Cross-platform)
Framework:  React Native + Expo
Files Read: touch-psychology.md, mobile-performance.md, platform-ios.md, platform-android.md

3 Principles I Will Apply:
1. FlatList with React.memo + useCallback for all lists
2. 48px touch targets, thumb zone for primary CTAs
3. Platform-specific navigation (edge swipe iOS, back button Android)

Anti-Patterns I Will Avoid:
1. ScrollView for lists → FlatList
2. Inline renderItem → Memoized
3. AsyncStorage for tokens → SecureStore

🔴 Can't fill the checkpoint? → GO BACK AND READ THE SKILL FILES.

🔧 Framework Decision Tree

WHAT ARE YOU BUILDING?
        │
        ├── Need OTA updates + rapid iteration + web team
        │   └── ✅ React Native + Expo
        │
        ├── Need pixel-perfect custom UI + performance critical
        │   └── ✅ Flutter
        │
        ├── Deep native features + single platform focus
        │   ├── iOS only → SwiftUI
        │   └── Android only → Kotlin + Jetpack Compose
        │
        ├── Existing RN codebase + new features
        │   └── ✅ React Native (bare workflow)
        │
        └── Enterprise + existing Flutter codebase
            └── ✅ Flutter

For complete decision trees: decision-trees.md

📋 Pre-Development Checklist

Before Starting ANY Mobile Project

• Platform confirmed? (iOS / Android / Both)
• Framework chosen? (RN / Flutter / Native)
• Navigation pattern decided? (Tabs / Stack / Drawer)
• State management selected? (Zustand / Redux / Riverpod / BLoC)
• Offline requirements known?
• Deep linking planned from day one?
• Target devices defined? (Phone / Tablet / Both)

Before Every Screen

• Touch targets ≥ 44-48px?
• Primary CTA in thumb zone?
• Loading state exists?
• Error state with retry exists?
• Offline handling considered?
• Platform conventions followed?

Before Release

• console.log removed?
• SecureStore for sensitive data?
• SSL pinning enabled?
• Lists optimized (memo, keyExtractor)?
• Memory cleanup on unmount?
• Tested on low-end devices?
• Accessibility labels on all interactive elements?

📚 Reference Files

For deeper guidance on specific areas:

Remember: Mobile users are impatient, interrupted, and using imprecise fingers on small screens. Design for the WORST conditions: bad network, one hand, bright sun, low battery. If it works there, it works everywhere.

🧠 AGI Framework Integration

Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

Decision Tree:

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags mobile-design <relevant-tags>

Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

Agent Team Collaboration

• Strategy: This skill communicates via the shared memory system.
• Orchestration: Invoked by orchestrator via intelligent routing.
• Context Sharing: Always read previous agent outputs from memory before starting.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns