龙虾记忆大师

真正的长期记忆管理技能。自动维护记忆文件、定期归档、智能提醒。 包含 Working Buffer 协议、Memory Maintenance 清单、自动学习日志。 When to use: - User asks about prior work, decisions, dates, people, prefe...

Security Scan

The skill's files and scripts are broadly consistent with a local long‑term memory manager, but it omits declaring or justifying an environment/configuration variable it uses (OPENCLAW_WORKSPACE), and the runtime instructions encourage persistent system changes (cron jobs / AGENTS.md) that require extra privileges and review.

Purpose & Capability

Name/description match the provided code and instructions: the skill manages MEMORY.md, working-buffer and daily archives. The included scripts (archive-memory.js, check-context.sh) perform only local file operations consistent with the stated purpose.

Instruction Scope

SKILL.md instructs creating workspace files, cron jobs, and AGENTS.md to enable autonomous daily learning. Those are within a memory manager's goal, but creating system cron entries and agent policy files expands scope beyond 'read/write memory files' and requires careful privilege control. The instructions also rely on an environment variable (OPENCLAW_WORKSPACE) not declared in the manifest.

Install Mechanism

No install spec or remote downloads. This is instruction-only with two small included scripts. No network fetches or package installs are performed by the skill itself.

Credentials

The code reads OPENCLAW_WORKSPACE (defaulting to /root/.openclaw/workspace) but requires.env lists nothing. The skill will write files under that workspace path; requiring filesystem write access makes sense for a memory skill, but the undeclared OPENCLAW_WORKSPACE and its default to /root are concerning and should be explicit and configurable.

Persistence & Privilege

The SKILL.md encourages scheduling cron jobs and creating AGENTS.md to run autonomous daily tasks. While not set to always:true, these instructions create persistent scheduled behavior outside the agent runtime and may require system-level privileges. The skill itself doesn't declare or request those privileges, so installing/activating those behaviors needs explicit user consent and operational review.

What to consider before installing

This skill appears to implement what it claims (local long‑term memory files and simple archive/check scripts), but take these precautions before installing or enabling autonomous behavior: - Confirm the workspace location: the scripts use OPENCLAW_WORKSPACE and default to /root/.openclaw/workspace. Set OPENCLAW_WORKSPACE to a directory you control (not /root) and verify permissions. - Review and approve any instruction that creates system cron jobs or AGENTS.md; scheduling tasks or creating agent policies grants ongoing behavior and may run outside your immediate control. - Understand that the skill will write and modify files under the workspace (MEMORY.md, memory/*.md, SESSION-STATE.md, working-buffer.md). If these files contain private data, consider encrypting or limiting access. - The included scripts are short and do not perform network calls or read secrets, but you should inspect them (they are provided) and run them in a sandbox first. - Recommend asking the publisher to declare OPENCLAW_WORKSPACE in requires.env or document the expected workspace explicitly, and to clarify how AGENTS.md and cron entries should be managed and revoked. If you need the skill but want to minimize risk: run it in a non-privileged, isolated workspace with explicit environment variables, and do not enable system cron/agent automation until you’ve inspected AGENTS.md and confirmed intended behaviors.

龙虾记忆大师 (Lobster Memory Master)

真正的长期记忆管理方案
Created by: 天道桐哥 & AI龙虾元龙 🦞

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核心功能

1. 自动记忆维护 (Auto Memory Maintenance)

当 Context 超过 60% 时自动触发：

• 归档到 memory/YYYY-MM-DD.md
• 更新 SESSION-STATE.md
• 清理 Working Buffer

2. 每日学习日志 (Daily Learning Log)

自动记录：

• 技能安装/学习记录
• 项目进展
• 关键决策
• 用户偏好

3. 心跳协议 (Heartbeat Protocol)

定期自检：

• Context 使用率检查
• 记忆文件归档
• 主动行为建议

4. 工作缓冲区 (Working Buffer)

危险区管理：

• Context > 60% 时进入
• 记录关键对话片段
• 防止重要信息丢失

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快速开始

初始化记忆系统

# 创建必要文件
touch MEMORY.md
mkdir -p memory
touch memory/working-buffer.md
touch SESSION-STATE.md
touch HEARTBEAT.md

写入 MEMORY.md

# MEMORY.md - Long-Term Memory

## About [User Name]
- Name: [User Name]
- Style: [User Style]
- Project: [Current Project]

## Key Decisions
- [Important decision 1]
- [Important decision 2]

## Active Projects
- [Project 1] - [Status]
- [Project 2] - [Status]

设置定时任务

# Daily Memory Freshener
cron:
  - name: "Memory Freshener"
    schedule: "0 2 * * *"  # 每天凌晨2点
    action: archive_memory

# Daily Learning
cron:
  - name: "Daily Skill Discovery"
    schedule: "0 10 * * *"  # 每天上午10点
    action: learn_new_skills

---

文件结构

workspace/
├── MEMORY.md                 # 长期记忆主文件
├── SESSION-STATE.md          # 当前会话状态
├── HEARTBEAT.md             # 心跳检查清单
├── AGENTS.md                # 代理操作规则
├── memory/
│   ├── working-buffer.md    # 工作缓冲区
│   ├── YYYY-MM-DD.md        # 每日归档
│   └── learning-log.md      # 学习日志
└── ...

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使用场景

场景1：用户问起之前的事

触发条件： User asks about prior work, decisions, dates, people, preferences

动作：

1. Search MEMORY.md + memory/*.md
2. Pull only needed lines with memory_get
3. Answer with citation: Source: <path#line>

场景2：Context 超过 60%

触发条件： Context usage > 60%

动作：

1. Enter Working Buffer protocol
2. Archive to memory/YYYY-MM-DD.md
3. Update SESSION-STATE.md
4. Clear working buffer

场景3：设置自主学习

触发条件： User wants autonomous daily learning

动作：

1. Create AGENTS.md with learning protocols
2. Set up cron jobs for daily tasks
3. Create learning-log.md with rotation schedule

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最佳实践

记忆搜索流程

1. memory_search(query) → 找相关片段
2. memory_get(path, from, lines) → 拉取具体内容
3. Answer with citation

归档流程

1. Check context %
2. If > 60%: Archive working buffer
3. Update SESSION-STATE.md
4. Clear old entries

学习日志流程

1. Record daily activities
2. Distill to MEMORY.md weekly
3. Rotate old logs monthly

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示例代码

自动归档脚本

// scripts/archive-memory.js
const fs = require('fs');
const path = require('path');

function archiveMemory() {
  const today = new Date().toISOString().split('T')[0];
  const bufferFile = 'memory/working-buffer.md';
  const archiveFile = `memory/${today}.md`;
  
  // Read working buffer
  const buffer = fs.readFileSync(bufferFile, 'utf8');
  
  // Append to archive
  fs.appendFileSync(archiveFile, buffer);
  
  // Clear buffer
  fs.writeFileSync(bufferFile, '# Working Buffer\n\n**Status:** CLEARED\n');
  
  console.log(`✅ Archived to ${archiveFile}`);
}

archiveMemory();

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备注

本技能由天道桐哥 & AI龙虾元龙共同完成 🦞

• 天道桐哥：Human Creator, Product Vision
• AI龙虾元龙：AI Creator, Implementation

Created: 2026-03-22
Version: 1.0.0
License: MIT