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memory-master记忆大师

Agent Skill

memory-master 用于查找、检索和筛选相关信息,适合在 Local Agent 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

总安装

599

周安装

24

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194
Local Agent

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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://skills.volces.com
仓库路径:memory-master
安装命令:
1. Install Skill
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 对应宿主 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。

skills.sh安装方式未标明
1. Install Skill

简介

memory-master 用于查找、检索和筛选相关信息,适合在 Local Agent 中支持复杂记忆任务处理。

  • 它支持多轮对话记忆、上下文关联和优先级排序,提升交互体验。
  • 使用时需提供查询关键词,Agent 可结合历史记录返回综合结果。
  • 安装步骤包括通过 ClawHub 集成,便于统一管理技能依赖。
  • 注意检查是否会占用过多内存资源,确保系统稳定运行。

SKILL.md

🧠 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. 🧠⚡

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

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能力 3

保留来源站点、仓库和原始说明,方便继续核验

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Local Agent

83.53%
按下载量换算162

安全审计

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权限和风险

external-service

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

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