Skill: konzenkane/lobster-memory (version 1.0.0)

--- name: lobster-memory zhName: 龙虾记忆大师 description: | 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, preferences - Context exceeds 60% and needs compaction - Setting up autonomous daily learning and memory maintenance - Creating long-term memory system for AI assistant This skill transforms AI from stateless chatbot to stateful assistant with persistent memory. license: MIT authors: - 天道桐哥 (Human Creator) - AI龙虾元龙 (AI Creator) version: 1.0.0 created: 2026-03-22 --- # 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 🦞 --- ## 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 --- ## 快速开始 ### 初始化记忆系统 ```bash # 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 ```markdown # 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 ```yaml # 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 └── ... ``` --- ## 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 --- ## 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 ``` --- ## 示例代码 ### 自动归档脚本 ```javascript // 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(); ``` --- ## 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)