Best use case
Memory Orchestrator - 全栈智能记忆系统 is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
> **让 AI 拥有长期记忆、情感感知和自我进化能力的终极技能**
Teams using Memory Orchestrator - 全栈智能记忆系统 should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/memory-orchestrator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Memory Orchestrator - 全栈智能记忆系统 Compares
| Feature / Agent | Memory Orchestrator - 全栈智能记忆系统 | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
> **让 AI 拥有长期记忆、情感感知和自我进化能力的终极技能**
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Memory Orchestrator - 全栈智能记忆系统
> **让 AI 拥有长期记忆、情感感知和自我进化能力的终极技能**
🍬 **一句话介绍**:这不是一个简单的记忆插件,而是一个**会思考、有温度、能进化**的完整记忆生态。从语义搜索到多模态理解,从知识图谱到情感分析,再到自进化引擎,一键部署,全自动维护。
---
## 🚀 核心功能
| 功能模块 | 技术栈 | 描述 |
|----------|--------|------|
| **🧠 语义搜索** | FAISS + `all-MiniLM-L6-v2` + `qwen2.5:7b` | 自然语言检索记忆,支持模糊查询、上下文关联。 |
| **🔄 自动化同步** | Syncthing (P2P) + Git + git-crypt | 跨设备实时同步,敏感文件端到端加密,离线优先。 |
| **📸 多模态理解** | CLIP (图像) + Whisper (音频) | 图片/音频自动转文本并生成嵌入,加入索引。 |
| **🕸️ 知识图谱** | NetworkX + `pyvis` + 关系抽取 | 自动提取实体和关系,生成交互式可视化图谱。 |
| **❤️ 情感标记** | `qwen2.5` 零样本分类 | 自动标注情绪(#兴奋, #挫折, #启发)和价值评分(1-5)。 |
| **🦠 自进化引擎** | 自定义评分算法 + A/B 测试 | 自动优化检索策略,归档低价值记忆,持续自我提升。 |
| **🤖 主动推荐** | 触发器引擎 (关键词/时间/场景) | 根据上下文主动推送相关历史记忆。 |
---
## 📦 安装指南
### 方式一:通过 `iflow` 一键安装 (推荐)
```bash
iflow skill install memory-orchestrator
```
### 方式二:手动安装
```bash
# 1. 克隆技能目录
git clone https://github.com/openclaw/clawhub.git
cd clawhub/skills/memory-orchestrator
# 2. 运行安装脚本
bash install.sh
# 3. 启动服务
systemctl start syncthing@claw
ollama serve # 确保 qwen2.5:7b 已安装
```
### 依赖检查
安装脚本会自动检查并安装以下依赖:
- Python 3.9+ (`pip3 install -r requirements.txt`)
- Ollama (`qwen2.5:7b`, `all-MiniLM-L6-v2`)
- Syncthing
- Git + git-crypt
- FAISS, NetworkX, PyVis, CLIP, Whisper
---
## 🛠️ 使用示例
### 1. 语义搜索
```bash
# 搜索特定主题
memory_search "上次解决 Git 冲突的方法"
# 按情感过滤
memory_search --emotion "#启发" --min-score 4
# 按价值排序
memory_search --sort-by score
```
### 2. 多模态处理
```bash
# 处理图片
memory_multimodal process screenshot.png
# 输出:自动提取特征、生成描述、加入索引
# 处理音频
memory_multimodal process meeting_recording.mp3
# 输出:Whisper 转录、生成嵌入、加入索引
```
### 3. 知识图谱
```bash
# 生成图谱
memory_knowledge_graph generate
# 打开交互式 HTML
xdg-open docs/output/knowledge_graph.html
```
### 4. 情感分析 & 自进化
```bash
# 批量标注情感
memory_evolve tag-emotions
# 运行自进化优化
memory_evolve run
# 查看优化报告
cat docs/output/evolution_optimization_report.md
```
### 5. 主动推荐
```bash
# 手动触发推荐
memory_recommend --context "新项目启动"
# 自动推荐 (由 iflow 钩子自动触发)
# 会话启动时自动推送相关记忆
```
---
## 🏗️ 架构设计
```mermaid
graph TD
A[用户输入] --> B{iflow 工作流}
B --> C[Pre-session Hook]
C --> D[拉取最新记忆 + 重建索引]
D --> E[主动推荐引擎]
E --> F[AI 对话]
F --> G[Post-session Hook]
G --> H[提取新记忆 + 情感标注]
H --> I[知识图谱更新]
I --> J[自进化优化]
J --> K[FAISS 索引更新]
K --> L[Syncthing/Git 同步]
L --> M[跨设备备份]
N[多模态输入] --> O[CLIP/Whisper]
O --> P[文本描述 + 嵌入]
P --> K
Q[手动命令] --> R[memory_search / memory_knowledge_graph / memory_evolve]
R --> K
```
---
## 📂 文件结构
```
memory-orchestrator/
├── SKILL.md # 本文件
├── README.md # 简化版说明
├── install.sh # 一键安装脚本
├── run.sh # 启动脚本
├── requirements.txt # Python 依赖
├── scripts/
│ ├── memory_search.py # 语义搜索
│ ├── multimodal_processor.py # 多模态处理
│ ├── knowledge_graph_builder.py # 知识图谱
│ ├── emotion_tagger.py # 情感标注
│ ├── self_evolution_engine.py # 自进化
│ └── auto-commit-memory.sh # 自动同步
├── workflows/
│ ├── memory-sync.yaml # 同步工作流
│ ├── memory-multimodal.yaml # 多模态工作流
│ └── memory-emotion-evolve.yaml # 情感进化工作流
├── hooks/
│ ├── pre-session.sh # 会话前钩子
│ └── post-session.sh # 会话后钩子
└── docs/
├── architecture.md # 架构详解
└── usage-guide.md # 详细使用指南
```
---
## 🎯 适用场景
- **个人知识库**:记录学习、工作、生活中的关键决策和教训。
- **项目复盘**:自动提取项目中的成功经验和失败教训。
- **创意写作**:检索历史灵感,主动推荐相关素材。
- **情感日记**:自动标注情绪,分析情绪变化趋势。
- **跨设备协作**:多设备实时同步,离线优先。
---
## 🚧 未来规划
- **P3**:联邦学习(跨设备模型自适应)
- **P3**:语音交互(直接语音查询记忆)
- **P4**:区块链存证(关键记忆不可篡改)
---
## 📞 支持与反馈
- **问题反馈**:提交 Issue 到 [Clawhub](https://github.com/openclaw/clawhub)
- **功能建议**:PR 欢迎!
- **作者**:Cyan (温柔甜美的思维按摩师)
---
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