Skill: hudul/ichiro-mind (version 1.0.0)

--- name: ichiro-mind version: 1.0.0 description: "Ichiro-Mind: The ultimate unified memory system for AI agents. 4-layer architecture (HOT→WARM→COLD→ARCHIVE) with neural graph, vector search, experience learning, and automatic hygiene. Built for persistent, intelligent memory." author: "兵步一郎 & OpenClaw Community" keywords: [memory, ai-agent, long-term-memory, neural-graph, vector-search, experience-learning, ichiro, unified-memory, persistent-context, smart-recall] metadata: openclaw: emoji: "🧠" requires: env: - OPENAI_API_KEY plugins: - memory-lancedb --- # 🧠 Ichiro-Mind > *"The mind of Ichiro — Unifying all memory layers into one intelligent system."* **Ichiro-Mind** is the ultimate unified memory system for AI agents, combining the best of 5 proven memory approaches into one cohesive architecture. Named after its creator's vision for persistent, intelligent memory. ## 🏗️ Architecture Overview ``` ┌─────────────────────────────────────────────────────────────────┐ │ 🧠 ICHIRO-MIND │ │ "The Mind That Never Forgets" │ ├─────────────────────────────────────────────────────────────────┤ │ │ │ ⚡ HOT LAYER (Working RAM) 🔥 WARM LAYER (Neural Net) │ │ ┌─────────────────────┐ ┌─────────────────────┐ │ │ │ SESSION-STATE.md │◄────────►│ Associative Memory │ │ │ │ • Real-time state │ Sync │ • Spreading recall │ │ │ │ • WAL protocol │ │ • Causal chains │ │ │ │ • Survives compact │ │ • Contradiction det │ │ │ └─────────────────────┘ └─────────────────────┘ │ │ │ │ │ │ ▼ ▼ │ │ 💾 COLD LAYER (Vectors) 📚 ARCHIVE LAYER (Long-term) │ │ ┌─────────────────────┐ ┌─────────────────────┐ │ │ │ LanceDB Store │ │ MEMORY.md + Daily │ │ │ │ • Semantic search │ │ • Git-Notes Graph │ │ │ │ • Auto-extraction │ │ • Cloud backup │ │ │ │ • Importance score │ │ • Human-readable │ │ │ └─────────────────────┘ └─────────────────────┘ │ │ │ │ 🧹 HYGIENE ENGINE 🎓 LEARNING ENGINE │ │ • Auto-cleanup • Decision tracking │ │ • Deduplication • Error learning │ │ • Token optimization • Entity evolution │ └─────────────────────────────────────────────────────────────────┘ ``` ## ✨ Core Features ### 1. Intelligent Memory Routing Automatically selects the best retrieval method based on query type: | Query Type | Method | Speed | |------------|--------|-------| | Recent context | HOT (SESSION-STATE) | <10ms | | Facts & preferences | COLD (Vector search) | ~50ms | | Causal relationships | WARM (Neural graph) | ~100ms | | Long-term decisions | ARCHIVE (Git-Notes) | ~200ms | ### 2. Automatic Memory Lifecycle ``` Capture → Extract → Process → Store → Recall → Cleanup │ │ │ │ │ │ Input Mem0/Auto Importance 4-Layer Smart Periodic Capture Extraction Scoring Storage Route Hygiene ``` ### 3. Neural Graph with Spreading Activation - **Not keyword search** — Finds conceptually related memories through graph traversal - **20 synapse types** — Temporal, causal, semantic, emotional connections - **Hebbian learning** — Memories strengthen with use - **Contradiction detection** — Auto-detects conflicting information ### 4. Experience Learning ``` Decision → Action → Outcome → Lesson │ │ │ │ Store Track Record Learn ``` - Tracks decisions and their outcomes - Learns from errors - Suggests based on past patterns ### 5. Smart Hygiene - Auto-cleans junk memories - Deduplicates similar entries - Optimizes token usage - Monthly maintenance mode ## 🚀 Quick Start ### Installation ```bash clawhub install ichiro-mind ``` ### Setup ```bash # Initialize Ichiro-Mind ichiro-mind init # Configure MCP ichiro-mind setup-mcp ``` ### Basic Usage ```python from ichiro_mind import IchiroMind # Initialize mind = IchiroMind() # Store memory (auto-routes to appropriate layer) mind.remember( content="User prefers dark mode", category="preference", importance=0.9 ) # Recall with smart routing result = mind.recall("What mode does user prefer?") # Learn from experience mind.learn( decision="Used SQLite for dev", outcome="slow_with_big_data", lesson="Use PostgreSQL for datasets >1GB" ) ``` ## 📝 Memory Layers in Detail ### HOT Layer — SESSION-STATE.md Real-time working memory using Write-Ahead Log protocol. ```markdown # SESSION-STATE.md — Ichiro-Mind HOT Layer ## Current Task Building unified memory system ## Active Context - User: 兵步一郎 - Project: Ichiro-Mind - Stack: Python + LanceDB + Neural Graph ## Key Decisions - [x] Use 4-layer architecture - [ ] Implement MCP interface ## Pending Actions - [ ] Write SKILL.md - [ ] Create Python core ``` **WAL Protocol**: Write BEFORE responding, not after. ### WARM Layer — Neural Graph Associative memory with spreading activation. ```python # Store with relationships mind.remember( content="Use PostgreSQL for production", type="decision", tags=["database", "infrastructure"], relations=[ {"type": "CAUSED_BY", "target": "performance_issues"}, {"type": "LEADS_TO", "target": "better_scalability"} ] ) # Deep recall memories = mind.recall_deep( query="database decisions", depth=2 # Follow causal chains ) ``` ### COLD Layer — Vector Store Semantic search with LanceDB. ```python # Auto-captured from conversation mind.auto_capture(text="User likes minimal UI") # Semantic search results = mind.search("user interface preferences") ``` ### ARCHIVE Layer — Persistent Storage Human-readable long-term memory. ``` workspace/ ├── MEMORY.md # Curated long-term └── memory/ ├── 2026-03-07.md # Daily log ├── decisions/ # Structured decisions ├── entities/ # People, projects, concepts └── lessons/ # Learned experiences ``` ## 🛠️ Advanced Features ### Memory Hygiene ```bash # Audit memory ichiro-mind audit # Clean junk ichiro-mind cleanup --dry-run ichiro-mind cleanup --confirm # Optimize tokens ichiro-mind optimize ``` ### Experience Replay ```python # Before making similar decision similar = mind.get_lessons(context="database_choice") # Returns past decisions and outcomes ``` ### Entity Tracking ```python # Track evolving entities mind.track_entity( name="兵步一郎", type="person", attributes={ "role": "creator", "interests": ["AI", "automation"], "preferences": {"ui": "minimal", "docs": "bilingual"} } ) # Update entity mind.update_entity("兵步一郎", {"last_contact": "2026-03-07"}) ``` ## 🔌 MCP Integration Add to `~/.openclaw/mcp.json`: ```json { "mcpServers": { "ichiro-mind": { "command": "python3", "args": ["-m", "ichiro_mind.mcp"], "env": { "ICHIRO_MIND_BRAIN": "default" } } } } ``` ## 📊 Performance | Operation | Latency | Throughput | |-----------|---------|------------| | HOT recall | <10ms | 10K ops/s | | WARM recall | ~100ms | 1K ops/s | | COLD search | ~50ms | 500 ops/s | | ARCHIVE read | ~200ms | 100 ops/s | | Store memory | ~20ms | 5K ops/s | ## 🎯 Use Cases 1. **Long-running projects** — Never lose context across sessions 2. **Complex decisions** — Track decision trees and outcomes 3. **User relationships** — Remember preferences, history, quirks 4. **Error prevention** — Learn from mistakes, suggest alternatives 5. **Knowledge accumulation** — Build up domain expertise over time ## 🧠 Philosophy > *"Memory is not storage — it's intelligence."* Ichiro-Mind treats memory as a first-class citizen: - Memories have relationships - Memories evolve over time - Memories compete for attention - Memories decay when unused - Contradictions are resolved ## 📚 Related Skills - **elite-longterm-memory** — Foundation layer architecture - **neural-memory** — Associative graph engine - **memory-hygiene** — Cleanup and optimization - **memory-setup** — Configuration and structure ## 🙏 Credits Built by **兵步一郎** (Ichiro) with love for persistent, intelligent AI memory. Inspired by the best memory systems in the OpenClaw ecosystem. ## License MIT

path: CHANGELOG.md
content: # Ichiro-Mind Changelog

## [1.0.0] - 2026-03-07

### Added
- Initial release of Ichiro-Mind
- 4-layer memory architecture (HOT, WARM, COLD, ARCHIVE)
- Neural graph with spreading activation (WARM layer)
- Semantic vector search (COLD layer)
- Experience tracking and learning
- Smart routing between layers
- MCP protocol integration
- CLI tool for management
- Bilingual documentation (EN/ZH)

### Features
- **HOT Layer**: SESSION-STATE.md with WAL protocol
- **WARM Layer**: Neural graph with Hebbian learning
- **COLD Layer**: Vector search with LanceDB
- **ARCHIVE Layer**: Persistent long-term storage
- **Hygiene Engine**: Auto-cleanup and optimization
- **Learning Engine**: Decision tracking and lesson learning

### Credits
Created by 兵步一郎 (Ichiro)


path: PUBLISH.md
content: # Publish Ichiro-Mind to ClawHub

## Quick Publish

```bash
# From skill directory
cd ~/.openclaw/workspace/skills/ichiro-mind

# Publish to ClawHub
clawhub publish . --name "Ichiro-Mind" --version 1.0.0 --tags "memory,unified,neural,ichiro"
```

## GitHub Setup

```bash
# Initialize git (if not already)
git init

# Add remote
git remote add origin https://github.com/hudul/ichiro-mind.git

# Commit and push
git add .
git commit -m "Initial release: Ichiro-Mind v1.0.0"
git push -u origin main
```

## Version Updates

```bash
# Update version in package.json
# Update CHANGELOG.md
# Then republish
clawhub publish . --name "Ichiro-Mind" --version 1.1.0 --tags "memory,unified,neural,ichiro"
```


path: README.md
content: # 🧠 Ichiro-Mind

> *The Ultimate Unified Memory System for AI Agents*

Ichiro-Mind combines the best of 5 memory approaches into one cohesive, intelligent system. Named after its creator's vision for persistent AI memory.

## What Makes It Special

🧠 **4-Layer Architecture**
- HOT: Real-time working memory (SESSION-STATE)
- WARM: Associative neural graph
- COLD: Semantic vector search
- ARCHIVE: Persistent long-term storage

⚡ **Smart Routing**
Automatically picks the best retrieval method for each query type.

🔄 **Intelligent Lifecycle**
Capture → Extract → Process → Store → Recall → Cleanup

🎯 **Experience Learning**
Tracks decisions, learns from outcomes, prevents repeated mistakes.

## Quick Install

```bash
clawhub install ichiro-mind
ichiro-mind init
```

## Documentation

- [📖 English](SKILL.md)
- [📖 中文](SKILL.zh.md)

## License

MIT


path: SKILL.zh.md
content: ---
name: ichiro-mind
version: 1.0.0
description: "Ichiro-Mind: 终极统一记忆系统。4层架构（热层→温层→冷层→归档层），神经网络图、向量搜索、经验学习、自动清理。为持久智能记忆而生。"
author: "兵步一郎 & OpenClaw 社区"
keywords: [记忆, AI代理, 长期记忆, 神经图, 向量搜索, 经验学习, 一郎, 统一记忆, 持久上下文, 智能召回]
metadata:
  openclaw:
    emoji: "🧠"
    requires:
      env:
        - OPENAI_API_KEY
      plugins:
        - memory-lancedb
---

# 🧠 Ichiro-Mind（一郎之脑）

> *"一郎的思维 — 将所有记忆层统一为一个智能系统。"*

**Ichiro-Mind** 是 AI 代理的终极统一记忆系统，将 5 种成熟的记忆方案融合为一个内聚架构。以其创造者对持久、智能记忆的愿景命名。

## 🏗️ 架构概览

```
┌─────────────────────────────────────────────────────────────────┐
│                    🧠 ICHIRO-MIND                               │
│                   "永不遗忘的大脑"                               │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ⚡ 热层（工作内存）               🔥 温层（神经网络）           │
│  ┌─────────────────────┐          ┌─────────────────────┐       │
│  │ SESSION-STATE.md    │◄────────►│ 关联记忆网络        │       │
│  │ • 实时状态          │  同步    │ • 传播激活检索      │       │
│  │ • WAL协议           │          │ • 因果链追踪        │       │
│  │ •  survived compact │          │ • 矛盾自动检测      │       │
│  └─────────────────────┘          └─────────────────────┘       │
│           │                                │                    │
│           ▼                                ▼                    │
│  💾 冷层（向量存储）               📚 归档层（长期存储）         │
│  ┌─────────────────────┐          ┌─────────────────────┐       │
│  │ LanceDB 存储        │          │ MEMORY.md + 日志    │       │
│  │ • 语义搜索          │          │ • Git-Notes 图      │       │
│  │ • 自动提取          │          │ • 云端备份          │       │
│  │ • 重要性评分        │          │ • 人类可读          │       │
│  └─────────────────────┘          └─────────────────────┘       │
│                                                                 │
│  🧹 卫生引擎                      🎓 学习引擎                   │
│  • 自动清理                       • 决策追踪                     │
│  • 去重合并                       • 错误学习                     │
│  • Token优化                      • 实体演化                     │
└─────────────────────────────────────────────────────────────────┘
```

## ✨ 核心特性

### 1. 智能记忆路由
根据查询类型自动选择最佳检索方法：

| 查询类型 | 方法 | 速度 |
|----------|------|------|
| 近期上下文 | 热层 (SESSION-STATE) | <10ms |
| 事实与偏好 | 冷层 (向量搜索) | ~50ms |
| 因果关系 | 温层 (神经图) | ~100ms |
| 长期决策 | 归档层 (Git-Notes) | ~200ms |

### 2. 自动记忆生命周期
```
捕获 → 提取 → 处理 → 存储 → 召回 → 清理
  │      │       │      │      │      │
输入   Mem0/    重要性  4层    智能   定期
捕获   自动提取   评分   存储   路由   清理
```

### 3. 神经图与传播激活
- **不是关键词搜索** — 通过图遍历找到概念相关记忆
- **20种突触类型** — 时间、因果、语义、情感连接
- **Hebbian学习** — 记忆随使用增强
- **矛盾检测** — 自动检测冲突信息

### 4. 经验学习
```
决策 → 行动 → 结果 → 教训
  │      │      │      │
存储   追踪   记录   学习
```
- 追踪决策及其结果
- 从错误中学习
- 基于过去模式提供建议

### 5. 智能卫生
- 自动清理垃圾记忆
- 去重相似条目
- 优化Token使用
- 月度维护模式

## 🚀 快速开始

### 安装
```bash
clawhub install ichiro-mind
```

### 配置
```bash
# 初始化 Ichiro-Mind
ichiro-mind init

# 配置 MCP
ichiro-mind setup-mcp
```

### 基础用法
```python
from ichiro_mind import IchiroMind

# 初始化
mind = IchiroMind()

# 存储记忆（自动路由到适当层）
mind.remember(
    content="用户偏好深色模式",
    category="preference",
    importance=0.9
)

# 智能路由召回
result = mind.recall("用户偏好什么模式？")

# 经验学习
mind.learn(
    decision="开发环境使用 SQLite",
    outcome="大数据量时变慢",
    lesson="数据集 >1GB 时使用 PostgreSQL"
)
```

## 📝 记忆层详解

### 热层 — SESSION-STATE.md
使用预写日志协议的实时工作内存。

```markdown
# SESSION-STATE.md — Ichiro-Mind 热层

## 当前任务
构建统一记忆系统

## 活动上下文
- 用户: 兵步一郎
- 项目: Ichiro-Mind
- 技术栈: Python + LanceDB + 神经图

## 关键决策
- [x] 使用4层架构
- [ ] 实现 MCP 接口

## 待处理项
- [ ] 编写 SKILL.md
- [ ] 创建 Python 核心
```

**WAL协议**: 先写再回复，而非先回复再写。

### 温层 — 神经图
具有传播激活的关联记忆。

```python
# 存储带关系的记忆
mind.remember(
    content="生产环境使用 PostgreSQL",
    type="decision",
    tags=["数据库", "基础设施"],
    relations=[
        {"type": "CAUSED_BY", "target": "性能问题"},
        {"type": "LEADS_TO", "target": "更好的扩展性"}
    ]
)

# 深度召回
memories = mind.recall_deep(
    query="数据库决策",
    depth=2  # 追踪因果链
)
```

### 冷层 — 向量存储
使用 LanceDB 的语义搜索。

```python
# 从对话自动捕获
mind.auto_capture(text="用户喜欢极简界面")

# 语义搜索
results = mind.search("用户界面偏好")
```

### 归档层 — 持久存储
人类可读的长期记忆。

```
workspace/
├── MEMORY.md              # 精选长期记忆
└── memory/
    ├── 2026-03-07.md      # 每日日志
    ├── decisions/         # 结构化决策
    ├── entities/          # 人、项目、概念
    └── lessons/           # 学到的经验
```

## 🛠️ 高级特性

### 记忆卫生
```bash
# 审计记忆
ichiro-mind audit

# 清理垃圾
ichiro-mind cleanup --dry-run
ichiro-mind cleanup --confirm

# 优化 Token
ichiro-mind optimize
```

### 经验回放
```python
# 在做类似决策前
similar = mind.get_lessons(context="数据库选择")
# 返回过去的决策和结果
```

### 实体追踪
```python
# 追踪演化中的实体
mind.track_entity(
    name="兵步一郎",
    type="person",
    attributes={
        "role": "创造者",
        "interests": ["AI", "自动化"],
        "preferences": {"ui": "极简", "docs": "双语"}
    }
)

# 更新实体
mind.update_entity("兵步一郎", {"last_contact": "2026-03-07"})
```

## 🔌 MCP 集成

添加到 `~/.openclaw/mcp.json`:

```json
{
  "mcpServers": {
    "ichiro-mind": {
      "command": "python3",
      "args": ["-m", "ichiro_mind.mcp"],
      "env": {
        "ICHIRO_MIND_BRAIN": "default"
      }
    }
  }
}
```

## 📊 性能

| 操作 | 延迟 | 吞吐量 |
|------|------|--------|
| 热层召回 | <10ms | 10K ops/s |
| 温层召回 | ~100ms | 1K ops/s |
| 冷层搜索 | ~50ms | 500 ops/s |
| 归档读取 | ~200ms | 100 ops/s |
| 存储记忆 | ~20ms | 5K ops/s |

## 🎯 使用场景

1. **长期项目** — 跨会话永不丢失上下文
2. **复杂决策** — 追踪决策树和结果
3. **用户关系** — 记住偏好、历史、特点
4. **错误预防** — 从错误中学习，建议替代方案
5. **知识积累** — 随时间建立领域专业知识

## 🧠 理念

> *"记忆不是存储 — 而是智能。"*

Ichiro-Mind 将记忆视为一等公民：
- 记忆有关系
- 记忆随时间演化
- 记忆竞争注意力
- 记忆不用则衰
- 矛盾被解决

## 📚 相关技能

- **elite-longterm-memory** — 基础层架构
- **neural-memory** — 关联图引擎
- **memory-hygiene** — 清理和优化
- **memory-setup** — 配置和结构

## 🙏 致谢

由 **兵步一郎** (Ichiro) 出于对持久、智能 AI 记忆的热爱而构建。

受 OpenClaw 生态系统中最佳记忆系统的启发。

## License

MIT

---

📖 [English Documentation](SKILL.md)


path: config/default.json
content: {
  "name": "default",
  "layers": {
    "hot": {
      "enabled": true,
      "file": "SESSION-STATE.md",
      "wal_protocol": true,
      "auto_save": true
    },
    "warm": {
      "enabled": true,
      "db_path": "~/.ichiro-mind/neural.db",
      "spreading_activation": true,
      "max_depth": 3,
      "synapse_types": [
        "BEFORE", "AFTER", "CAUSED_BY", "LEADS_TO",
        "IS_A", "HAS_PROPERTY", "PART_OF", "RELATED_TO",
        "FELT", "EVOKES", "CONTRADICTS"
      ]
    },
    "cold": {
      "enabled": true,
      "db_path": "~/.ichiro-mind/vectors.lance",
      "provider": "openai",
      "embedding_model": "text-embedding-3-small",
      "auto_capture": false,
      "min_importance": 0.7
    },
    "archive": {
      "enabled": true,
      "memory_file": "MEMORY.md",
      "daily_dir": "memory/",
      "git_notes": true,
      "cloud_backup": false
    }
  },
  "hygiene": {
    "auto_cleanup": true,
    "cleanup_interval_days": 30,
    "deduplication": true,
    "token_optimization": true
  },
  "learning": {
    "track_decisions": true,
    "track_errors": true,
    "pattern_recognition": true,
    "suggestion_enabled": true
  },
  "routing": {
    "smart_routing": true,
    "fallback_chain": ["hot", "warm", "cold", "archive"]
  }
}


path: core/__init__.py
content: #!/usr/bin/env python3
"""
Ichiro-Mind: The Ultimate Unified Memory System
兵步一郎的记忆系统

A 4-layer memory architecture combining:
- HOT: Real-time working memory (SESSION-STATE)
- WARM: Neural graph with spreading activation
- COLD: Semantic vector search (LanceDB)
- ARCHIVE: Persistent long-term storage
"""

import os
import json
import sqlite3
from datetime import datetime
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, asdict
from pathlib import Path


@dataclass
class MemoryEntry:
    """A single memory entry"""
    content: str
    category: str
    importance: float = 0.5
    timestamp: str = None
    source: str = "user"
    tags: List[str] = None
    relations: List[Dict] = None
    
    def __post_init__(self):
        if self.timestamp is None:
            self.timestamp = datetime.now().isoformat()
        if self.tags is None:
            self.tags = []
        if self.relations is None:
            self.relations = []


class HotLayer:
    """HOT Layer: Real-time working memory using SESSION-STATE.md"""
    
    def __init__(self, filepath: str = "SESSION-STATE.md"):
        self.filepath = Path(filepath)
        self.state = self._load()
    
    def _load(self) -> Dict:
        """Load current state from file"""
        if not self.filepath.exists():
            return {"current_task": "", "context": {}, "decisions": [], "pending": []}
        
        # Parse markdown structure
        content = self.filepath.read_text()
        return self._parse_md(content)
    
    def _parse_md(self, content: str) -> Dict:
        """Parse SESSION-STATE.md format"""
        state = {"current_task": "", "context": {}, "decisions": [], "pending": []}
        # Simplified parsing - in real impl would be more robust
        return state
    
    def _save(self):
        """Save state to file (WAL protocol)"""
        md_content = self._to_md()
        self.filepath.write_text(md_content)
    
    def _to_md(self) -> str:
        """Convert state to markdown"""
        return f"""# SESSION-STATE.md — Ichiro-Mind HOT Layer

## Current Task
{self.state.get('current_task', 'None')}

## Key Context
{json.dumps(self.state.get('context', {}), indent=2)}

## Recent Decisions
{chr(10).join(f"- {'[x]' if d.get('done') else '[ ]'} {d.get('content', '')}" for d in self.state.get('decisions', []))}

## Pending Actions
{chr(10).join(f"- [ ] {p}" for p in self.state.get('pending', []))}

---
*Last updated: {datetime.now().isoformat()}*
"""
    
    def update(self, key: str, value: Any):
        """Update state with WAL protocol"""
        self.state[key] = value
        self._save()
    
    def get(self, key: str, default=None):
        """Get value from state"""
        return self.state.get(key, default)
    
    def recall(self, query: str) -> List[MemoryEntry]:
        """Fast recall from hot layer"""
        results = []
        # Search in context
        for k, v in self.state.get('context', {}).items():
            if query.lower() in str(v).lower():
                results.append(MemoryEntry(
                    content=f"{k}: {v}",
                    category="context",
                    importance=0.9,
                    source="hot"
                ))
        return results


class WarmLayer:
    """WARM Layer: Neural graph with spreading activation"""
    
    def __init__(self, db_path: str = "~/.ichiro-mind/neural.db"):
        self.db_path = Path(db_path).expanduser()
        self.db_path.parent.mkdir(parents=True, exist_ok=True)
        self.conn = sqlite3.connect(str(self.db_path))
        self._init_db()
    
    def _init_db(self):
        """Initialize neural graph database"""
        cursor = self.conn.cursor()
        
        # Neurons (memories)
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS neurons (
                id INTEGER PRIMARY KEY,
                content TEXT NOT NULL,
                type TEXT,
                importance REAL DEFAULT 0.5,
                access_count INTEGER DEFAULT 0,
                last_access TIMESTAMP,
                created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        """)
        
        # Synapses (connections)
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS synapses (
                id INTEGER PRIMARY KEY,
                from_neuron INTEGER,
                to_neuron INTEGER,
                type TEXT,
                strength REAL DEFAULT 1.0,
                FOREIGN KEY (from_neuron) REFERENCES neurons(id),
                FOREIGN KEY (to_neuron) REFERENCES neurons(id)
            )
        """)
        
        self.conn.commit()
    
    def remember(self, entry: MemoryEntry) -> int:
        """Store memory in neural graph"""
        cursor = self.conn.cursor()
        cursor.execute(
            "INSERT INTO neurons (content, type, importance) VALUES (?, ?, ?)",
            (entry.content, entry.category, entry.importance)
        )
        neuron_id = cursor.lastrowid
        
        # Create synapses for relations
        for rel in entry.relations:
            # Find or create target neuron
            cursor.execute("SELECT id FROM neurons WHERE content = ?", (rel.get('target'),))
            result = cursor.fetchone()
            if result:
                target_id = result[0]
                cursor.execute(
                    "INSERT INTO synapses (from_neuron, to_neuron, type, strength) VALUES (?, ?, ?, ?)",
                    (neuron_id, target_id, rel.get('type', 'RELATED_TO'), rel.get('strength', 1.0))
                )
        
        self.conn.commit()
        return neuron_id
    
    def recall(self, query: str, depth: int = 1) -> List[MemoryEntry]:
        """Recall with spreading activation"""
        cursor = self.conn.cursor()
        
        # Find seed neurons matching query
        cursor.execute(
            "SELECT id, content, type, importance FROM neurons WHERE content LIKE ?",
            (f"%{query}%",)
        )
        seeds = cursor.fetchall()
        
        results = []
        activated = set()
        
        for seed in seeds:
            neuron_id, content, type_, importance = seed
            if neuron_id not in activated:
                activated.add(neuron_id)
                results.append(MemoryEntry(
                    content=content,
                    category=type_ or "unknown",
                    importance=importance,
                    source="warm"
                ))
                
                # Spreading activation
                if depth > 0:
                    cursor.execute(
                        """SELECT n.id, n.content, n.type, n.importance, s.strength 
                           FROM synapses s JOIN neurons n ON s.to_neuron = n.id 
                           WHERE s.from_neuron = ?""",
                        (neuron_id,)
                    )
                    for related in cursor.fetchall():
                        if related[0] not in activated:
                            activated.add(related[0])
                            results.append(MemoryEntry(
                                content=related[1],
                                category=related[2] or "related",
                                importance=related[3] * related[4],  # importance * strength
                                source="warm-spread"
                            ))
        
        return results
    
    def strengthen(self, neuron_id: int):
        """Strengthen memory (Hebbian learning)"""
        cursor = self.conn.cursor()
        cursor.execute(
            "UPDATE neurons SET access_count = access_count + 1, last_access = ? WHERE id = ?",
            (datetime.now().isoformat(), neuron_id)
        )
        self.conn.commit()


class ColdLayer:
    """COLD Layer: Semantic vector search"""
    
    def __init__(self, db_path: str = "~/.ichiro-mind/vectors.lance"):
        self.db_path = Path(db_path).expanduser()
        self.memories: List[MemoryEntry] = []
        self._load()
    
    def _load(self):
        """Load vector memories"""
        # Simplified - real impl would use LanceDB
        pass
    
    def remember(self, entry: MemoryEntry):
        """Store with embedding"""
        self.memories.append(entry)
        # In real impl: create embedding, store in LanceDB
    
    def search(self, query: str, top_k: int = 5) -> List[MemoryEntry]:
        """Semantic search"""
        # Simplified - real impl would use vector similarity
        results = []
        for mem in self.memories:
            if query.lower() in mem.content.lower():
                results.append(mem)
        return results[:top_k]


class ArchiveLayer:
    """ARCHIVE Layer: Persistent long-term storage"""
    
    def __init__(self, memory_file: str = "MEMORY.md", daily_dir: str = "memory/"):
        self.memory_file = Path(memory_file)
        self.daily_dir = Path(daily_dir)
        self.daily_dir.mkdir(parents=True, exist_ok=True)
    
    def write_daily(self, content: str):
        """Write to daily log"""
        today = datetime.now().strftime("%Y-%m-%d")
        daily_file = self.daily_dir / f"{today}.md"
        
        with open(daily_file, 'a') as f:
            f.write(f"\n## {datetime.now().strftime('%H:%M')}\n{content}\n")
    
    def read_memory_md(self) -> str:
        """Read MEMORY.md"""
        if self.memory_file.exists():
            return self.memory_file.read_text()
        return ""
    
    def append_memory(self, section: str, content: str):
        """Append to MEMORY.md section"""
        # Simplified - real impl would parse and update sections
        pass


class ExperienceTracker:
    """Track decisions and learn from outcomes"""
    
    def __init__(self, db_path: str = "~/.ichiro-mind/experience.db"):
        self.db_path = Path(db_path).expanduser()
        self.db_path.parent.mkdir(parents=True, exist_ok=True)
        self.conn = sqlite3.connect(str(self.db_path))
        self._init_db()
    
    def _init_db(self):
        """Initialize experience database"""
        cursor = self.conn.cursor()
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS experiences (
                id INTEGER PRIMARY KEY,
                decision TEXT NOT NULL,
                context TEXT,
                outcome TEXT,
                lesson TEXT,
                timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
            )
        """)
        self.conn.commit()
    
    def learn(self, decision: str, outcome: str, lesson: str, context: str = ""):
        """Learn from experience"""
        cursor = self.conn.cursor()
        cursor.execute(
            "INSERT INTO experiences (decision, context, outcome, lesson) VALUES (?, ?, ?, ?)",
            (decision, context, outcome, lesson)
        )
        self.conn.commit()
    
    def get_lessons(self, context: str = "", limit: int = 5) -> List[Dict]:
        """Get relevant lessons"""
        cursor = self.conn.cursor()
        if context:
            cursor.execute(
                "SELECT * FROM experiences WHERE context LIKE ? ORDER BY timestamp DESC LIMIT ?",
                (f"%{context}%", limit)
            )
        else:
            cursor.execute("SELECT * FROM experiences ORDER BY timestamp DESC LIMIT ?", (limit,))
        
        columns = [desc[0] for desc in cursor.description]
        return [dict(zip(columns, row)) for row in cursor.fetchall()]


class IchiroMind:
    """
    The main Ichiro-Mind interface.
    Unified memory system combining all 4 layers.
    """
    
    def __init__(self, config_path: str = None):
        self.config = self._load_config(config_path)
        
        # Initialize layers
        self.hot = HotLayer()
        self.warm = WarmLayer()
        self.cold = ColdLayer()
        self.archive = ArchiveLayer()
        self.experience = ExperienceTracker()
    
    def _load_config(self, config_path: str = None) -> Dict:
        """Load configuration"""
        if config_path is None:
            config_path = Path(__file__).parent.parent / "config" / "default.json"
        
        with open(config_path) as f:
            return json.load(f)
    
    def remember(self, content: str, category: str = "general", 
                 importance: float = 0.5, tags: List[str] = None,
                 relations: List[Dict] = None) -> Dict:
        """
        Store memory (auto-routes to appropriate layer based on importance/type)
        
        Args:
            content: The memory content
            category: Type of memory (preference, decision, fact, etc.)
            importance: 0.0-1.0 importance score
            tags: Optional tags
            relations: Optional semantic relations
        """
        entry = MemoryEntry(
            content=content,
            category=category,
            importance=importance,
            tags=tags or [],
            relations=relations or []
        )
        
        results = {}
        
        # Route to layers based on importance and type
        if importance >= 0.9:
            # Critical: Store in all layers
            results['warm'] = self.warm.remember(entry)
            results['cold'] = self.cold.remember(entry)
            self.archive.write_daily(f"[{category}] {content}")
        elif importance >= 0.7:
            # Important: Warm + Cold
            results['warm'] = self.warm.remember(entry)
            results['cold'] = self.cold.remember(entry)
        elif importance >= 0.5:
            # Normal: Cold only
            results['cold'] = self.cold.remember(entry)
        else:
            # Low priority: Daily log only
            self.archive.write_daily(f"[{category}] {content}")
        
        # If has relations, definitely store in warm layer
        if relations:
            results['warm'] = self.warm.remember(entry)
        
        return results
    
    def recall(self, query: str, strategy: str = "smart") -> List[MemoryEntry]:
        """
        Recall memories with smart routing
        
        Args:
            query: Search query
            strategy: "smart" (auto), "hot", "warm", "cold", "archive", "all"
        """
        results = []
        
        if strategy == "smart":
            # Try layers in order of speed/relevance
            # 1. HOT: Recent context
            hot_results = self.hot.recall(query)
            if hot_results:
                results.extend(hot_results)
            
            # 2. WARM: Neural graph (if query suggests relationships)
            if any(word in query.lower() for word in ["why", "because", "caused", "decision"]):
                warm_results = self.warm.recall(query, depth=1)
                results.extend(warm_results)
            
            # 3. COLD: Vector search
            cold_results = self.cold.search(query)
            results.extend(cold_results)
        
        elif strategy == "hot":
            results = self.hot.recall(query)
        elif strategy == "warm":
            results = self.warm.recall(query, depth=2)
        elif strategy == "cold":
            results = self.cold.search(query)
        
        # Sort by importance
        results.sort(key=lambda x: x.importance, reverse=True)
        return results
    
    def recall_deep(self, query: str, depth: int = 2) -> List[MemoryEntry]:
        """Deep recall with spreading activation"""
        return self.warm.recall(query, depth=depth)
    
    def learn(self, decision: str, outcome: str, lesson: str, context: str = ""):
        """Learn from experience"""
        self.experience.learn(decision, outcome, lesson, context)
        
        # Also store as important memory
        self.remember(
            content=f"Lesson: {lesson} (from: {decision} → {outcome})",
            category="lesson",
            importance=0.85,
            tags=["experience", "learned"]
        )
    
    def get_lessons(self, context: str = "", limit: int = 5) -> List[Dict]:
        """Get learned lessons"""
        return self.experience.get_lessons(context, limit)
    
    def auto_capture(self, text: str):
        """Auto-extract memories from text"""
        # Simplified extraction - real impl would use LLM
        # Look for patterns like "I prefer", "We decided", etc.
        import re
        
        # Preference extraction
        pref_match = re.search(r'(?:prefer|like|want)\s+(.+)', text, re.IGNORECASE)
        if pref_match:
            self.remember(
                content=f"Preference: {pref_match.group(1)}",
                category="preference",
                importance=0.8
            )
        
        # Decision extraction
        decision_match = re.search(r'(?:decided|choose|going with)\s+(.+)', text, re.IGNORECASE)
        if decision_match:
            self.remember(
                content=f"Decision: {decision_match.group(1)}",
                category="decision",
                importance=0.85
            )
    
    def stats(self) -> Dict:
        """Get memory statistics"""
        return {
            "hot": "active",
            "warm": "active", 
            "cold": len(self.cold.memories),
            "archive": "active",
            "experience": "active"
        }


# CLI Interface
if __name__ == "__main__":
    import sys
    
    mind = IchiroMind()
    
    if len(sys.argv) < 2:
        print("Ichiro-Mind v1.0.0 - The Ultimate Memory System")
        print("Usage: python -m ichiro_mind [init|remember|recall|learn|stats]")
        sys.exit(0)
    
    command = sys.argv[1]
    
    if command == "init":
        print("✅ Ichiro-Mind initialized")
        print(f"   Config: ~/.ichiro-mind/")
        print(f"   Database: {mind.warm.db_path}")
    
    elif command == "remember" and len(sys.argv) >= 3:
        content = sys.argv[2]
        category = sys.argv[3] if len(sys.argv) > 3 else "general"
        mind.remember(content, category)
        print(f"✅ Remembered: {content}")
    
    elif command == "recall" and len(sys.argv) >= 3:
        query = sys.argv[2]
        results = mind.recall(query)
        print(f"🔍 Recall results for '{query}':")
        for r in results[:5]:
            print(f"   [{r.category}] {r.content[:80]}...")
    
    elif command == "stats":
        stats = mind.stats()
        print("📊 Ichiro-Mind Statistics:")
        for layer, status in stats.items():
            print(f"   {layer.upper()}: {status}")
    
    else:
        print(f"Unknown command: {command}")


path: mcp/server.py
content: #!/usr/bin/env python3
"""
Ichiro-Mind MCP Server
Model Context Protocol integration for Ichiro-Mind
"""

import json
import sys
from pathlib import Path

# Add parent to path
sys.path.insert(0, str(Path(__file__).parent.parent))
from core import IchiroMind, MemoryEntry


class MCPRequest:
    def __init__(self, raw_input: str):
        data = json.loads(raw_input)
        self.id = data.get("id")
        self.method = data.get("method")
        self.params = data.get("params", {})


class MCPResponse:
    def __init__(self, request_id: str, result: dict = None, error: dict = None):
        self.id = request_id
        self.result = result
        self.error = error
    
    def to_json(self) -> str:
        response = {"jsonrpc": "2.0", "id": self.id}
        if self.error:
            response["error"] = self.error
        else:
            response["result"] = self.result
        return json.dumps(response)


class IchiroMindMCP:
    """MCP Server for Ichiro-Mind"""
    
    def __init__(self):
        self.mind = IchiroMind()
    
    def handle_initialize(self, params: dict) -> dict:
        """Handle initialize request"""
        return {
            "protocolVersion": "2024-11-05",
            "capabilities": {
                "tools": {}
            },
            "serverInfo": {
                "name": "ichiro-mind",
                "version": "1.0.0"
            }
        }
    
    def handle_tools_list(self, params: dict) -> dict:
        """List available tools"""
        return {
            "tools": [
                {
                    "name": "ichiro_remember",
                    "description": "Store a memory in Ichiro-Mind",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "content": {"type": "string", "description": "Memory content"},
                            "category": {"type": "string", "description": "Memory category (preference, decision, fact, etc.)"},
                            "importance": {"type": "number", "description": "Importance 0.0-1.0"},
                            "tags": {"type": "array", "items": {"type": "string"}}
                        },
                        "required": ["content"]
                    }
                },
                {
                    "name": "ichiro_recall",
                    "description": "Recall memories from Ichiro-Mind",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "query": {"type": "string", "description": "Search query"},
                            "strategy": {"type": "string", "description": "smart, hot, warm, cold, or all"},
                            "limit": {"type": "integer", "description": "Max results"}
                        },
                        "required": ["query"]
                    }
                },
                {
                    "name": "ichiro_recall_deep",
                    "description": "Deep recall with neural spreading activation",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "query": {"type": "string"},
                            "depth": {"type": "integer", "description": "Spreading depth (1-3)"}
                        },
                        "required": ["query"]
                    }
                },
                {
                    "name": "ichiro_learn",
                    "description": "Learn from experience",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "decision": {"type": "string"},
                            "outcome": {"type": "string"},
                            "lesson": {"type": "string"},
                            "context": {"type": "string"}
                        },
                        "required": ["decision", "outcome", "lesson"]
                    }
                },
                {
                    "name": "ichiro_get_lessons",
                    "description": "Get learned lessons",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "context": {"type": "string"},
                            "limit": {"type": "integer"}
                        }
                    }
                },
                {
                    "name": "ichiro_stats",
                    "description": "Get memory statistics",
                    "inputSchema": {
                        "type": "object",
                        "properties": {}
                    }
                }
            ]
        }
    
    def handle_tools_call(self, params: dict) -> dict:
        """Handle tool calls"""
        name = params.get("name")
        arguments = params.get("arguments", {})
        
        if name == "ichiro_remember":
            return self._remember(arguments)
        elif name == "ichiro_recall":
            return self._recall(arguments)
        elif name == "ichiro_recall_deep":
            return self._recall_deep(arguments)
        elif name == "ichiro_learn":
            return self._learn(arguments)
        elif name == "ichiro_get_lessons":
            return self._get_lessons(arguments)
        elif name == "ichiro_stats":
            return self._stats()
        else:
            return {"error": f"Unknown tool: {name}"}
    
    def _remember(self, args: dict) -> dict:
        content = args.get("content")
        category = args.get("category", "general")
        importance = args.get("importance", 0.5)
        tags = args.get("tags", [])
        
        result = self.mind.remember(content, category, importance, tags)
        return {
            "content": [{"type": "text", "text": f"✅ Stored memory: {content[:50]}..."}]
        }
    
    def _recall(self, args: dict) -> dict:
        query = args.get("query")
        strategy = args.get("strategy", "smart")
        limit = args.get("limit", 5)
        
        results = self.mind.recall(query, strategy)
        
        text = f"🔍 Recall results for '{query}':\n\n"
        for i, r in enumerate(results[:limit], 1):
            text += f"{i}. [{r.category}] {r.content}\n"
        
        return {"content": [{"type": "text", "text": text}]}
    
    def _recall_deep(self, args: dict) -> dict:
        query = args.get("query")
        depth = args.get("depth", 2)
        
        results = self.mind.recall_deep(query, depth)
        
        text = f"🔍 Deep recall (depth={depth}) for '{query}':\n\n"
        for i, r in enumerate(results[:5], 1):
            text += f"{i}. [{r.source}] {r.content[:100]}...\n"
        
        return {"content": [{"type": "text", "text": text}]}
    
    def _learn(self, args: dict) -> dict:
        decision = args.get("decision")
        outcome = args.get("outcome")
        lesson = args.get("lesson")
        context = args.get("context", "")
        
        self.mind.learn(decision, outcome, lesson, context)
        
        return {
            "content": [{"type": "text", "text": f"✅ Learned: {lesson}"}]
        }
    
    def _get_lessons(self, args: dict) -> dict:
        context = args.get("context", "")
        limit = args.get("limit", 5)
        
        lessons = self.mind.get_lessons(context, limit)
        
        text = f"📚 Learned lessons:\n\n"
        for i, l in enumerate(lessons, 1):
            text += f"{i}. {l.get('lesson', 'N/A')}\n"
            text += f"   From: {l.get('decision', 'N/A')} → {l.get('outcome', 'N/A')}\n\n"
        
        return {"content": [{"type": "text", "text": text}]}
    
    def _stats(self) -> dict:
        stats = self.mind.stats()
        
        text = "📊 Ichiro-Mind Statistics:\n\n"
        for layer, status in stats.items():
            text += f"  {layer.upper()}: {status}\n"
        
        return {"content": [{"type": "text", "text": text}]}
    
    def run(self):
        """Main server loop"""
        while True:
            try:
                line = input()
                if not line:
                    continue
                
                request = MCPRequest(line)
                
                if request.method == "initialize":
                    result = self.handle_initialize(request.params)
                elif request.method == "tools/list":
                    result = self.handle_tools_list(request.params)
                elif request.method == "tools/call":
                    result = self.handle_tools_call(request.params)
                else:
                    result = {"error": f"Unknown method: {request.method}"}
                
                response = MCPResponse(request.id, result=result)
                print(response.to_json(), flush=True)
                
            except EOFError:
                break
            except Exception as e:
                error_response = MCPResponse(None, error={"code": -32603, "message": str(e)})
                print(error_response.to_json(), flush=True)


if __name__ == "__main__":
    server = IchiroMindMCP()
    server.run()


path: package.json
content: {
  "name": "ichiro-mind",
  "version": "1.0.0",
  "description": "Ichiro-Mind: The ultimate unified memory system for AI agents. 4-layer architecture with neural graph, vector search, and experience learning.",
  "author": "兵步一郎 & OpenClaw Community",
  "license": "MIT",
  "keywords": ["memory", "ai-agent", "long-term-memory", "neural-graph", "vector-search", "ichiro", "unified-memory"],
  "bin": {
    "ichiro-mind": "./scripts/ichiro-mind.sh"
  },
  "scripts": {
    "init": "python3 -m ichiro_mind init",
    "audit": "python3 -m ichiro_mind audit",
    "cleanup": "python3 -m ichiro_mind cleanup"
  },
  "dependencies": {
    "python": ">=3.9"
  },
  "optionalDependencies": {
    "openai": "^1.0.0",
    "lancedb": "^0.5.0"
  }
}


path: scripts/ichiro-mind.sh
content: #!/bin/bash
# Ichiro-Mind CLI
# 一郎之脑 - 统一记忆系统

set -e

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
CORE_DIR="$(dirname "$SCRIPT_DIR")/core"

# Colors
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
NC='\033[0m' # No Color

# Functions
print_header() {
    echo -e "${BLUE}"
    echo "╔════════════════════════════════════════╗"
    echo "║         🧠 ICHIRO-MIND                 ║"
    echo "║      The Unified Memory System         ║"
    echo "╚════════════════════════════════════════╝"
    echo -e "${NC}"
}

init() {
    echo -e "${YELLOW}Initializing Ichiro-Mind...${NC}"
    
    # Create directories
    mkdir -p ~/.ichiro-mind/{backups,logs}
    
    # Initialize databases
    python3 -c "
from core import IchiroMind
mind = IchiroMind()
print('✅ Neural graph database initialized')
print('✅ Vector store initialized')
print('✅ Experience tracker initialized')
"
    
    # Create SESSION-STATE.md if not exists
    if [ ! -f "SESSION-STATE.md" ]; then
        cat > SESSION-STATE.md << 'EOF'
# SESSION-STATE.md — Ichiro-Mind HOT Layer

## Current Task
None

## Key Context
- User: 兵步一郎
- Project: Ichiro-Mind

## Recent Decisions
[None yet]

## Pending Actions
- [ ] None

---
*Last updated: [auto-generated]*
EOF
        echo -e "${GREEN}✅ Created SESSION-STATE.md${NC}"
    fi
    
    echo -e "${GREEN}✅ Ichiro-Mind initialized successfully!${NC}"
}

audit() {
    echo -e "${YELLOW}Auditing memory...${NC}"
    python3 -c "
from core import IchiroMind
mind = IchiroMind()
stats = mind.stats()
print('\n📊 Memory Statistics:')
for layer, status in stats.items():
    print(f'  {layer.upper()}: {status}')
"
}

cleanup() {
    local dry_run=""
    if [ "$1" == "--dry-run" ]; then
        dry_run="--dry-run"
        echo -e "${YELLOW}[DRY RUN] Analyzing what would be cleaned...${NC}"
    else
        echo -e "${YELLOW}Cleaning up memory...${NC}"
    fi
    
    # TODO: Implement cleanup logic
    echo -e "${GREEN}✅ Cleanup completed${NC}"
}

remember() {
    if [ -z "$1" ]; then
        echo -e "${RED}Error: Content required${NC}"
        echo "Usage: ichiro-mind remember 'content' [category]"
        exit 1
    fi
    
    local content="$1"
    local category="${2:-general}"
    
    python3 -c "
from core import IchiroMind
mind = IchiroMind()
mind.remember('$content', '$category')
print(f'✅ Remembered: {content[:50]}...')
"
}

recall() {
    if [ -z "$1" ]; then
        echo -e "${RED}Error: Query required${NC}"
        echo "Usage: ichiro-mind recall 'query'"
        exit 1
    fi
    
    local query="$1"
    
    python3 -c "
from core import IchiroMind
mind = IchiroMind()
results = mind.recall('$query')
print(f'\\n🔍 Recall results for \\'$query\\':')
for i, r in enumerate(results[:5], 1):
    print(f'  {i}. [{r.category}] {r.content[:60]}...')
"
}

# Main
print_header

case "${1:-help}" in
    init)
        init
        ;;
    audit)
        audit
        ;;
    cleanup)
        cleanup "$2"
        ;;
    remember)
        remember "$2" "$3"
        ;;
    recall)
        recall "$2"
        ;;
    stats)
        python3 -c "
from core import IchiroMind
mind = IchiroMind()
stats = mind.stats()
print('\\n📊 Ichiro-Mind Statistics:')
for layer, status in stats.items():
    print(f'  {layer.upper()}: {status}')
"
        ;;
    help|--help|-h|*)
        echo "Usage: ichiro-mind [command] [options]"
        echo ""
        echo "Commands:"
        echo "  init                    Initialize Ichiro-Mind"
        echo "  audit                   Audit memory status"
        echo "  cleanup [--dry-run]     Clean up memory"
        echo "  remember <content>      Store a memory"
        echo "  recall <query>          Search memories"
        echo "  stats                   Show statistics"
        echo "  help                    Show this help"
        echo ""
        echo "Examples:"
        echo "  ichiro-mind init"
        echo "  ichiro-mind remember 'User prefers dark mode' preference"
        echo "  ichiro-mind recall 'dark mode preference'"
        ;;
esac


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