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Agent Skill · 研究检索

memory-master(记忆大师)

memory-master 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

memory-master 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 4,020。

总安装 36,966周安装 1,481GitHub Stars 2下载 11,966核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
2.6.5

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:memory-master(记忆大师)
来源仓库:https://github.com/lizhelong0907/memory-master
安装命令:
openclaw skills install memory-master
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install memory-master

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

memory-master 具备结构化索引与自动学习能力,持续沉淀经验与修正错误。适合希望 Agent 自主记录问题、优化策略并填补能力缺口的场景。支持启发式回忆与知识缺口自动学习机制。安装命令为 openclaw skills install memory-master,需读写本地记忆库权限。

自动学习可能引入偏差,建议设置人工审核环节。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install memory-master;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

memory-master 怎么安装?

在终端执行 openclaw skills install memory-master。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

memory-master 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

memory-master 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

memory-master 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

memory-master 用的人多吗?

公开数据是累计安装 36,966 次,近一周安装 1,481 次,下载 11,966 次,GitHub 2 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

memory-master 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
memory-master

记忆大师 · 本页

36,9662调用外部服务MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

FeatureDescription
📝 Structured Memory"Cause → Change → Todo" format for every entry
🔄 Auto Index SyncWrite once, index updates automatically
🎯 Zero Token WasteRead only what you need, nothing more
⚡ Heuristic RecallProactively finds relevant memories when context is missing
🧠 Auto LearningWhen local knowledge is insufficient, automatically search web to learn and update knowledge base
🔓 Full ControlAll 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:

EventWrite LocationContent
skill_completememory/knowledge/记录学到了什么新技能/方法
skill_errormemory/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

StepActionTrigger
1Parse user queryUser asks question
2Check: do I have context?If uncertain
3Read daily-index.mdContext missing
4Locate relevant topicFound in index
5Read target date fileKnow the date
6Restore contextGot info
7Answer userComplete

Knowledge Base Flow Summary

StepActionTrigger
1Parse user queryUser asks question
2Search knowledge-indexAlways check first
3Found solution?Yes → Solve / No → Continue
4Tell user "I don't know yet"No solution
5Search web & learnGet knowledge
6Write to knowledge/*.mdNew knowledge
7Update knowledge-indexKeep index in sync
8Solve the problemComplete

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

MetricTraditionalMemory Master v1.2
Recall precision~30%~95%
Token cost per recallHigh (full file)Near zero (targeted)
Proactive recall❌✅ (heuristic)
Knowledge learning❌✅
API dependenciesVector DB / OpenAINone
Setup complexityHighZero
LatencyVariableInstant

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:

StepActionResult
1BackupOriginal files saved to .memory-master-backup/
2Heartbeat MigrationHeartbeat content moved from AGENTS.md to HEARTBEAT.md
3AGENTS.md OptimizationRemove duplicates, outdated rules, streamline language
4MEMORY.md TransformationConvert to pure lessons/experience repository
5Memory StructureCreate 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. 🧠⚡

平台分布

OpenClaw

94.91%
按下载量换算11,357

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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