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memory-tiering内存分层

Agent Skill

memory-tiering 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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

457,662

周安装

19,659

GitHub Stars

9

下载量

160,417
OpenClaw

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。

GitHub

来源数

2

许可证

MIT-0

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-tiering(内存分层)
来源仓库:https://github.com/sarielwang93/memory-tiering
安装命令:
openclaw skills install memory-tiering
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-tiering

简介

自动多层内存管理(HOT、WARM、COLD)。使用此技能在内存操作或压缩期间组织、修剪和归档上下文。

SKILL.md

name
memory-tiering
description
Automated multi-tiered memory management (HOT, WARM, COLD). Use this skill to organize, prune, and archive context during memory operations or compactions.

Memory Tiering Skill 🧠⚖️

This skill implements a dynamic, three-tiered memory architecture to optimize context usage and retrieval efficiency.

The Three Tiers

  1. 🔥 HOT (memory/hot/HOT_MEMORY.md):

* Focus: Current session context, active tasks, temporary credentials, immediate goals. * Management: Updated frequently. Pruned aggressively once tasks are completed.

  1. 🌡️ WARM (memory/warm/WARM_MEMORY.md):

* Focus: User preferences (Hui's style, timezone), core system inventory, stable configurations, recurring interests. * Management: Updated when preferences change or new stable tools are added.

  1. ❄️ COLD (MEMORY.md):

* Focus: Long-term archive, historical decisions, project milestones, distilled lessons. * Management: Updated during archival phases. Detail is replaced by summaries.

Workflow: Organize-Memory

Whenever a memory reorganization is triggered (manual or post-compaction), follow these steps:

Step 1: Ingest & Audit

  • Read all three tiers and recent daily logs (memory/YYYY-MM-DD.md).
  • Identify "Dead Context" (completed tasks, resolved bugs).

Step 2: Tier Redistribution

  • Move to HOT: Anything requiring immediate attention in the next 2-3 turns.
  • Move to WARM: New facts about the user or system that are permanent.
  • Move to COLD: Completed high-level project summaries.

Step 3: Pruning & Summarization

  • Remove granular details from COLD.
  • Ensure credentials in HOT point to their root files rather than storing raw secrets (if possible).

Step 4: Verification

  • Ensure no critical information was lost during the move.
  • Verify that HOT is now small enough for efficient context use.

Usage Trigger

  • Trigger manually with: "Run memory tiering" or "整理记忆层级".
  • Trigger automatically after any /compact command.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

OpenClaw

86.61%
按下载量换算138,937

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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