🎁 Get the FREE AI Skills Starter Guide — Subscribe →
BytesAgainBytesAgain
BytesAgain › Skills › Memory Master
🦀 ClawHub

Memory Master

by @lizhelong0907

Local memory system with structured indexing and auto-learning. Auto-write, heuristic recall, auto learning when knowledge is insufficient. Compatible with s...

Versionv2.6.5
Downloads1,919
Stars⭐ 2
#clawhub
TERMINAL
clawhub install memory-master

📖 About This Skill

name: memory-master version: 2.6.1 description: "Local memory system with structured indexing and auto-learning. Auto-write, heuristic recall, auto learning when knowledge is insufficient. Compatible with self-improving-agent: auto-records skill completions and errors to knowledge base." author: 李哲龙 tags: [memory, recall, indexing, context]

🧠 Memory Master — The Precision Memory System

*Transform your AI agent from forgetful to photographic.*

The Problem

Most AI agents suffer from memory amnesia:

  • ❌ Can't remember what you discussed yesterday
  • ❌ Loads entire memory files, burning tokens
  • ❌ Fuzzy search returns irrelevant results
  • ❌ No structure, just raw text dumps
  • ❌ Waits for user to trigger recall, never proactively remembers

    • You deserve better.

    The Solution: Memory Master v1.2.4

    A precision-targeted memory architecture with optional network learning capability.

    ✨ Key Features

    | Feature | Description | |---------|-------------| | 📝 Structured Memory | "Cause → Change → Todo" format for every entry | | 🔄 Auto Index Sync | Write once, index updates automatically | | 🎯 Zero Token Waste | Read only what you need, nothing more | | ⚡ Heuristic Recall | Proactively finds relevant memories when context is missing | | 🧠 Auto Learning | When local knowledge is insufficient, automatically search web to learn and update knowledge base | | 🔓 Full Control | All files visible/editable/deletable. No auto network calls. |

    The Memory Format

    Daily Memory: memory/daily/YYYY-MM-DD.md

    Format: 

    ## [日期] 主题

  • 因：原因/背景
    • 

  • 改：做了什么、改了什么
    • 

  • 待：待办/后续
    •

    Example: 

    ## [2026-03-03] 记忆系统升级

  • 因：原记忆目录混乱，查找困难
    • 

  • 改：目录调整为 daily/ + knowledge/，上传 v1.1.0
    • 

  • 待：检查 ClawHub 描述
    •

    Why this format?

  • 一目了然 (一目了然 = instantly clear at a glance)
  • 逻辑清晰：因 → 改 → 待
  • 通用模板，适用于任何场景

    • The Index Format

    Index: memory/daily-index.md

    Format: 

    # 记忆索引
  • 主题名 → daily/日期.md,日期.md
    •

    Example: 

    # 记忆索引
  • 记忆系统升级 → daily/2026-03-03.md
    • 

  • 飞书配置 → daily/2026-03-02.md,daily/2026-03-03.md
    • 

  • 电商网站 → daily/2026-03-02.md
    •

    Rules:

  • 逗号分隔多天
  • 只有一个一级标题：记忆索引
  • 简洁清晰，一眼定位

    • Heuristic Recall Protocol

    When to Trigger Recall

    DON'T wait for user to say "yesterday" or "remember"

    Trigger recall when: 1. User mentions a topic you don't have context for 2. Current conversation references something past 3. You feel "I'm not sure I have this information" 4. User asks about "that", "the project", "the skill"

    Recall Flow

    用户问题 → 发现上下文缺失 → 读 index 定位主题 → 读取记忆文件 → 恢复上下文 → 回答

    Example: 

    User: "那个 skill 你觉得还有什么要改的吗？"
    1. 思考：我知道用户指哪个 skill 吗？→ 不知道，上下文没有
2. 读 index → 找到"记忆系统升级 → daily/2026-03-03.md"
3. 读取文件 → 恢复记忆
4. 回答："根据昨天记录，我们..."

    Key Principle

    "When you realize you don't know, go check the index."

    Knowledge Base System

    Knowledge Structure

    memory/knowledge/
├── knowledge-index.md
└── *.md (knowledge entries)

    Knowledge Index: memory/knowledge-index.md

    极简格式 - 关键字列表： 

    # 知识库索引
  • clawhub
    • 

  • oauth
    • 

  • react
    •

    When to Read Knowledge Base

    启发式：当前上下文没有相关信息时才读

    1. 用户有要求 → 按用户要求执行 2. 用户没要求 → 检查上下文有没有规则 3. 上下文没有 → 搜索知识库索引 4. 找到对应项 → 读取知识库文件执行

  • 上下文有 → 直接用
  • 上下文没有 → 搜索引 → 读知识库文件 → 执行

    • Problem Solving Flow

    用户问题 → 上下文有？→ 有：直接解决 / 无：搜索引 → 有知识？→ 有：解决 / 无：自动网络搜索学习 → 写知识库 → 更新索引 → 解决问题

    Example: 

    User: "怎么上传 skill 到 ClawHub？"
    1. 上下文有 clawhub 信息？→ 有（刚学过）→ 直接回答
2. 不用读知识库
    
User: "怎么实现 OAuth？"
    1. 上下文有 OAuth 信息？→ 没有
2. 搜 knowledge-index → 没有 OAuth
3. 告知用户："我还不会，先去查一下"
4. 网络搜索学习
5. 写入 knowledge/oauth.md
6. 更新 knowledge-index
7. 开始和用户沟通解决方案

    Write Flow

    When to Write

    Write immediately after: 1. Discussion reaches a conclusion 2. Decision is made 3. Action item is assigned 4. Something important happens 5. Learned something new (check before every response)

    ⚠️ IMPORTANT: Auto-Trigger Write

    DO NOT wait for user to remind you!

    Before every response, quickly check: "Did I learn anything new in this conversation?" If yes, write it.

    Write IMMEDIATELY when any of the above happens. This is NOT optional.

    Skill Event Triggers (Auto-Record)

    When a skill completes or errors, automatically record to knowledge:

    | Event | Write Location | Content | |-------|---------------|---------| | skill_complete | memory/knowledge/ | 记录学到了什么新技能/方法 | | skill_error | memory/knowledge/ | 记录错误原因和解决方案 |

    统一写入知识库，因为都是"学到新知识"。

    Write Steps

    1. Detect conclusion/action (automatically, every time) 2. Format using "因-改-待" template 3. Write to memory/daily/YYYY-MM-DD.md 4. Update daily-index.md (add new topic or append date)

    IMPORTANT: Always update index when writing to daily memory!

    Update MEMORY.md (if needed)

    When writing to MEMORY.md: 1. Check for duplicate/outdated rules 2. Merge and clean up 3. Keep it minimal

    Example

    讨论：我们要改进记忆系统，决定把目录分成 daily/ 和 knowledge/
结论：改完了，今天上传到 GitHub 和 ClawHub
    写入：

    [2026-03-04] 记忆系统升级
    

  • 因：原记忆目录混乱，查找困难
    • 

  • 改：目录调整为 daily/ + knowledge/，上传 v1.1.0
    • 

  • 待：检查 ClawHub 描述
    • 更新索引：

  • 记忆系统升级 → daily/2026-03-03.md,daily/2026-03-04.md
    •

    Recall Flow Summary

    | Step | Action | Trigger | |------|--------|---------| | 1 | Parse user query | User asks question | | 2 | Check: do I have context? | If uncertain | | 3 | Read daily-index.md | Context missing | | 4 | Locate relevant topic | Found in index | | 5 | Read target date file | Know the date | | 6 | Restore context | Got info | | 7 | Answer user | Complete |

    Knowledge Base Flow Summary

    | Step | Action | Trigger | |------|--------|---------| | 1 | Parse user query | User asks question | | 2 | Search knowledge-index | Always check first | | 3 | Found solution? | Yes → Solve / No → Continue | | 4 | Tell user "I don't know yet" | No solution | | 5 | Search web & learn | Get knowledge | | 6 | Write to knowledge/*.md | New knowledge | | 7 | Update knowledge-index | Keep index in sync | | 8 | Solve the problem | Complete |

    File Structure

    ~/.openclaw/workspace/
├── AGENTS.md              # Your rules
├── MEMORY.md              # Long-term memory (main session only)
├── memory/
│   ├── daily/             # Daily records
│   │   ├── 2026-03-02.md
│   │   ├── 2026-03-03.md
│   │   └── 2026-03-04.md
│   ├── knowledge/         # Knowledge base
│   │   ├── react-basics.md
│   │   └── flask-api.md
│   ├── daily-index.md     # Daily memory index
│   └── knowledge-index.md # Knowledge index

    Comparison

    | Metric | Traditional | Memory Master v1.2 | |--------|-------------|---------------------| | Recall precision | ~30% | ~95% | | Token cost per recall | High (full file) | Near zero (targeted) | | Proactive recall | ❌ | ✅ (heuristic) | | Knowledge learning | ❌ | ✅ | | API dependencies | Vector DB / OpenAI | None | | Setup complexity | High | Zero | | Latency | Variable | Instant |

    Requirements

    None. This skill works with pure OpenClaw:

  • ✅ OpenClaw installed
  • ✅ Workspace configured
  • ✅ That's it!

    • No external APIs. No embeddings. No costs.

    Installation

    1. Install Skill

    clawdhub install memory-master

    2. Auto-Initialize (Enhanced for v2.6.0)

    # This will automatically:

    - Migrate heartbeat rules from AGENTS.md to HEARTBEAT.md
    

    - Optimize AGENTS.md (deduplicate, streamline, restructure)
    

    - Convert MEMORY.md to pure lessons/experience repository
    

    - Create memory directory structure and index files
    

    - Backup original files to .memory-master-backup/ directory
    
clawdhub init memory-master

    What the enhanced initialization does:

    | Step | Action | Result | |------|--------|--------| | 1 | Backup | Original files saved to .memory-master-backup/ | | 2 | Heartbeat Migration | Heartbeat content moved from AGENTS.md to HEARTBEAT.md | | 3 | AGENTS.md Optimization | Remove duplicates, outdated rules, streamline language | | 4 | MEMORY.md Transformation | Convert to pure lessons/experience repository | | 5 | Memory Structure | Create memory/ directories and index files |

    Post-initialization files: 

    ~/.openclaw/workspace/
├── AGENTS.md              # Optimized behavior rules + memory system rules
├── MEMORY.md              # Pure lessons/experience repository
├── HEARTBEAT.md           # Heartbeat tasks and guidelines
├── memory/
│   ├── daily/             # Daily records (YYYY-MM-DD.md format)
│   ├── knowledge/         # Knowledge base (*.md files)
│   ├── daily-index.md     # Memory index
│   └── knowledge-index.md # Knowledge index

    Or manually (advanced users): 

    # 1. Run the initialization script directly
node ~/.agents/skills/memory-master/scripts/init.js
    2. Or manually copy templates
    
cp ~/.agents/skills/memory-master/templates/optimized-agents.md ~/.openclaw/workspace/AGENTS.md
cp ~/.agents/skills/memory-master/templates/heartbeat-template.md ~/.openclaw/workspace/HEARTBEAT.md
cp ~/.agents/skills/memory-master/templates/memory-lessons.md ~/.openclaw/workspace/MEMORY.md
    3. Create memory directories
    
mkdir -p ~/.openclaw/workspace/memory/daily
mkdir -p ~/.openclaw/workspace/memory/knowledge
    4. Create index files
    
cp ~/.agents/skills/memory-master/templates/daily-index.md ~/.openclaw/workspace/memory/daily-index.md
cp ~/.agents/skills/memory-master/templates/knowledge-index.md ~/.openclaw/workspace/memory/knowledge-index.md

    ⚠️ Security & Privacy

  • 100% Local: All memory/knowledge stored in local workspace files only. Nothing leaves your machine except your initiated web searches.
  • Auto-Write to Local: This is a FEATURE — prevents information loss. Same as OpenClaw's native memory system.
  • Auto Learning: When local knowledge is insufficient, automatically search web to learn. Writes results to local knowledge base only.
  • Full Transparency: All files visible/editable/deletable by user anytime.
  • Safe: No data uploaded, only search queries sent to search engines.
  • User Control: User explicitly authorizes web searches ("我去查一下", "let me search the web") before any network activity

    • Triggers

    Memory Recall

  • "that"
  • "上次"
  • "之前"
  • "昨天"
  • "earlier"
  • Or: when you realize you don't have the context

    • Knowledge Learning

  • When you can't find answer in knowledge base
  • User asks something new

    • Memory Writing

  • Discussion reaches conclusion
  • Decision made
  • Action assigned

    • Best Practices

    1. Write immediately — Don't wait, write right after conclusion 2. Keep it brief — One line per point, but core info preserved 3. Use the template — 因 → 改 → 待 4. Update index — Always sync after writing 5. Heuristic recall — Don't wait for user to trigger 6. Learn proactively — When you don't know, say it and learn

    Compression Detection (v2.6.3+)

    ⚠️ Important: Must run after EVERY response!

    Run after every response:

    node ~/.agents/skills/memory-master/scripts/detect.js

    Display status at the bottom of every response:

  • 50%: 📝 上下文使用率：50% - 是否需要记录记忆或知识库？
  • 70%: ⚠️ 上下文使用率：70% - 建议记录当前进度
  • 85%: 🚨 上下文使用率：85% - 请立即记录当前进度！

    • Why this matters:

  • Prevents context loss from compression
  • Reminds user to record memories before data is lost
  • Works with heartbeat but runs more frequently

    • The Memory Master Promise

    > *"An AI agent is only as good as its memory. Give your agent a memory system that never forgets, never wastes, and always delivers exactly what's needed."*

    Memory Master v1.2.0 — Because remembering everything is just as important as learning something new. 🧠⚡

    💡 Examples

    ``` 讨论：我们要改进记忆系统，决定把目录分成 daily/ 和 knowledge/ 结论：改完了，今天上传到 GitHub 和 ClawHub

    写入：

    📋 Tips & Best Practices

    1. Write immediately — Don't wait, write right after conclusion 2. Keep it brief — One line per point, but core info preserved 3. Use the template — 因 → 改 → 待 4. Update index — Always sync after writing 5. Heuristic recall — Don't wait for user to trigger 6. Learn proactively — When you don't know, say it and learn