Update documentation: reflect Layer 3.5 completion
Updated README.md and ARCHITECTURE.md to document Layer 3.5 (Integrated Profile) implementation. README.md changes: - Updated header to reflect Layers 1-3.5 complete - Added Layer 3.5 feature section - Added get_profile to MCP tools list - Added Layer 3.5 usage examples with sample output - Updated architecture overview with Layer 3.5 - Added design philosophy explanation ARCHITECTURE.md changes: - Updated layer overview diagram with Layer 3.5 - Added comprehensive Layer 3.5 section: - Purpose and problem solved - Data model (UserProfile, TraitScore) - Integration logic (5 extraction methods) - Caching strategy with update triggers - Usage patterns for AI - Design philosophy - Updated implementation strategy (Phase 3.5) - Updated code organization to reflect current structure - Updated version metadata Layer 3.5 provides unified user profile by integrating Layers 1-3 data, implementing "internal complexity, external simplicity" design philosophy.
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Claude
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README.md
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README.md
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AI memory system with psychological analysis for Claude via MCP.
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**Current: Layers 1-3 Complete** - Memory storage, AI interpretation, and personality analysis.
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**Current: Layers 1-3.5 Complete** - Memory storage, AI interpretation, personality analysis, and integrated profile.
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## Features
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- 📈 **Pattern Recognition**: Analyzes memory patterns to build user profile
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- 💾 **Historical Tracking**: Save and compare analyses over time
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### Layer 3.5: Integrated Profile
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- 🎯 **Essential Summary**: Unified view of personality, interests, and values
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- 🤖 **AI-Optimized**: Primary tool for AI to understand the user
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- ⚡ **Smart Caching**: Auto-updates only when necessary
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- 🔍 **Flexible Access**: Detailed data still accessible when needed
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### General
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- 🛠️ **MCP Integration**: Works seamlessly with Claude Code
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- 🧪 **Well-tested**: Comprehensive test coverage
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- `save_user_analysis` - Save Big Five personality analysis
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- `get_user_analysis` - Retrieve latest personality profile
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### Layer 3.5: Integrated Profile (1 tool)
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- `get_profile` - **Primary tool**: Get integrated user profile with essential summary
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## Usage Examples in Claude Code
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### Layer 1: Simple Memory
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get_user_analysis()
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```
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### Layer 3.5: Integrated Profile (Recommended)
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```
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# Get essential user profile - AI's primary tool
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get_profile()
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# Returns:
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{
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"dominant_traits": [
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{"name": "openness", "score": 0.8},
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{"name": "conscientiousness", "score": 0.7},
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{"name": "extraversion", "score": 0.4}
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],
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"core_interests": ["Rust", "architecture", "design", "system", "memory"],
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"core_values": ["simplicity", "efficiency", "maintainability"],
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"key_memory_ids": ["01H...", "01H...", ...],
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"data_quality": 0.85
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}
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```
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**Usage Pattern:**
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- AI normally uses `get_profile()` to understand the user
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- For specific details, AI can call `get_memory(id)`, `list_memories()`, etc.
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- Profile auto-updates when needed (10+ memories, new analysis, or 7+ days)
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## Big Five Personality Traits
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- **Openness**: Creativity, curiosity, openness to new experiences
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@@ -133,9 +166,15 @@ Multi-layer system design:
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- **Layer 1** ✅ Complete: Pure memory storage
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- **Layer 2** ✅ Complete: AI interpretation with priority scoring
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- **Layer 3** ✅ Complete: Big Five personality analysis
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- **Layer 3.5** ✅ Complete: Integrated profile (unified summary)
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- **Layer 4** 🔵 Planned: Game systems and companion features
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- **Layer 5** 🔵 Future: Distribution and sharing
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**Design Philosophy**: "Internal complexity, external simplicity"
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- Layers 1-3 handle detailed data collection and analysis
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- Layer 3.5 provides a simple, unified view for AI consumption
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- Detailed data remains accessible when needed
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See [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md) for details.
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## Documentation
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│ Layer 4a: Game Systems │ 🔵 Planned
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│ (Ranking, rarity, XP, visualization) │
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├─────────────────────────────────────────┤
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│ Layer 3.5: Integrated Profile │ ✅ Complete
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│ (Unified summary for AI consumption) │
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├─────────────────────────────────────────┤
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│ Layer 3: User Evaluation │ ✅ Complete
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│ (Big Five personality analysis) │
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├─────────────────────────────────────────┤
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@@ -171,6 +174,127 @@ pub struct UserAnalysis {
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---
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## Layer 3.5: Integrated Profile
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**Status**: ✅ **Complete**
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### Purpose
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Layer 1-3のデータを統合し、本質のみを抽出した統一プロファイル。「内部は複雑、表面はシンプル」の設計哲学を実現。
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### Problem Solved
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Layer 1-3は独立して動作するが、バラバラのデータをAIが毎回解釈する必要があった。Layer 3.5は統合された1つの答えを提供し、効率性とシンプルさを両立。
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### Data Model
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```rust
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pub struct UserProfile {
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// 性格の本質（Big Five上位3特性）
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pub dominant_traits: Vec<TraitScore>,
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// 関心の核心（最頻出トピック5個）
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pub core_interests: Vec<String>,
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// 価値観の核心（高priority メモリから抽出、5個）
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pub core_values: Vec<String>,
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// 重要メモリID（証拠、上位10個）
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pub key_memory_ids: Vec<String>,
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// データ品質（0.0-1.0、メモリ数と分析有無で算出）
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pub data_quality: f32,
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pub last_updated: DateTime<Utc>,
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}
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pub struct TraitScore {
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pub name: String, // "openness", "conscientiousness", etc.
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pub score: f32, // 0.0-1.0
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}
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```
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### Integration Logic
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**1. Dominant Traits Extraction**
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- Big Fiveから上位3特性を自動選択
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- スコアでソート
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**2. Core Interests Extraction**
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- メモリコンテンツから頻度分析
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- AI interpretationは2倍の重み
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- 上位5個を抽出
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**3. Core Values Extraction**
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- priority_score >= 0.7 のメモリから抽出
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- 価値関連キーワードをフィルタリング
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- 上位5個を抽出
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**4. Key Memories**
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- priority_scoreでソート
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- 上位10個のIDを保持（証拠として）
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**5. Data Quality Score**
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- メモリ数: 50個で1.0（それ以下は比例）
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- 性格分析あり: +0.5
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- 加重平均で算出
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### Caching Strategy
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**Storage**: SQLite `user_profiles` テーブル（1行のみ）
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**Update Triggers**:
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1. 10個以上の新しいメモリ追加
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2. 新しい性格分析の保存
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3. 7日以上経過
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**Flow**:
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```
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get_profile()
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↓
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キャッシュ確認
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↓
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更新必要？ → No → キャッシュを返す
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↓ Yes
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Layer 1-3から再生成
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↓
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キャッシュ更新
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↓
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新しいプロファイルを返す
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```
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### MCP Tools
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- `get_profile` - **Primary tool**: Get integrated profile
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### Usage Pattern
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**通常使用（効率的）**:
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```
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AI: get_profile()を呼ぶ
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→ ユーザーの本質を理解
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→ 適切な応答を生成
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```
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**詳細確認（必要時）**:
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```
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AI: get_profile()で概要を把握
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→ 疑問がある
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→ get_memory(id)で詳細確認
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→ list_memories()で全体確認
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```
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### Design Philosophy
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**"Internal complexity, external simplicity"**
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- 内部: 複雑な分析、頻度計算、重み付け
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- 表面: シンプルな1つのJSON
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- AIは基本的にget_profile()のみ参照
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- 柔軟性: 詳細データへのアクセスも可能
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**Efficiency**:
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- 頻繁な再計算を避ける（キャッシング）
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- 必要時のみ更新（スマートトリガー）
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- AI が迷わない（1つの明確な答え）
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---
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## Layer 4a: Game Systems
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**Status**: 🔵 **Planned**
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@@ -264,6 +388,16 @@ pub struct Companion {
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- [x] `get_user_analysis` tool
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- [x] Historical tracking support
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### Phase 3.5: Layer 3.5 ✅ (Complete)
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- [x] UserProfile data structure
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- [x] Integration logic (traits, interests, values)
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- [x] Frequency analysis for topic extraction
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- [x] Value keyword extraction
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- [x] Data quality scoring
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- [x] Caching mechanism (user_profiles table)
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- [x] Smart update triggers
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- [x] `get_profile` MCP tool
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### Phase 4: Layers 4-5 (Next)
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- [ ] Game mechanics (Layer 4a)
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- [ ] Companion system (Layer 4b)
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@@ -337,16 +471,27 @@ pub struct Companion {
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```
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src/
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├── core/ # Layer 1: Pure storage
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├── ai/ # Layer 2: AI features (future)
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├── evaluation/ # Layer 3: User diagnosis (future)
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├── game/ # Layer 4a: Game systems (future)
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├── companion/ # Layer 4b: Companion (future)
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└── mcp/ # MCP server (all layers)
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├── core/
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│ ├── memory.rs # Layer 1: Memory struct
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│ ├── store.rs # Layer 1-3.5: SQLite operations
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│ ├── analysis.rs # Layer 3: UserAnalysis (Big Five)
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│ ├── profile.rs # Layer 3.5: UserProfile (integrated)
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│ ├── error.rs # Error types
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│ └── mod.rs # Module exports
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├── mcp/
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│ ├── base.rs # MCP server (all layers)
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│ └── mod.rs # Module exports
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├── lib.rs # Library root
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└── main.rs # CLI application
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```
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**Future layers**:
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- Layer 4a: `src/game/` - Game systems
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- Layer 4b: `src/companion/` - Companion features
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- Layer 5: `src/distribution/` - Sharing mechanisms
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---
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**Version**: 0.2.0
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**Last Updated**: 2025-11-06
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**Current Status**: Layers 1-3 Complete, Layer 4 Planned
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**Current Status**: Layers 1-3.5 Complete, Layer 4 Planned
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