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memory-dream-consolidation记忆梦巩固

Agent Skill

memory-dream-consolidation 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

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周安装

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下载量

1,204
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-dream-consolidation(记忆梦巩固)
来源仓库:https://github.com/xwz119/memory-dream-consolidation
安装命令:
openclaw skills install memory-dream-consolidation
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-dream-consolidation

简介

memory-dream-consolidation 实现 OpenClaw 代理的结构化内存分类与定期整合(Dream)。

  • 适用于新代理初始化内存系统或 MEMORY.md 结构混乱时的重构需求。
  • 支持自动聚类与层级划分,提升记忆可读性与检索准确性。
  • 安装命令为 openclaw skills install memory-dream-consolidation,需修改 MEMORY.md 权限。
  • 使用前应备份原文件,并在低峰期执行以防影响实时任务处理。

SKILL.md

name
memory-dream
description
Structured memory classification and periodic consolidation (Dream) for OpenClaw agents. Activate when setting up a new agent's memory system, when MEMORY.md is getting too long (over 200 lines), when doing periodic memory maintenance during heartbeats, or when the user says "整理记忆" "记忆维护" "dream" "memory cleanup". NOT for daily note writing (just write normally) or conversation recall (use memory_search).

Memory Dream — 记忆分类与整合系统

Inspired by Claude Code's Dream memory consolidation system. Adapted for OpenClaw's file-based memory architecture.

Memory Classification — 四类记忆

Every memory entry belongs to one of four types. Tag each entry when writing.

TypeWhat to StoreWhereExample
userUser's role, preferences, knowledge, communication styleUSER.md / MEMORY.md"Jim prefers casual Chinese, works at Amazon, timezone UTC+8"
feedbackCorrections AND confirmations — what works and what doesn'tAGENTS.md / MEMORY.md"append over write for Feishu docs. Why: write overwrites history. How: always use feishu_doc append"
projectOngoing work, goals, deadlines, decisionsdaily notes / MEMORY.md"France trip Jun 19-27, 4 people, budget TBD — optimized for museum closure days"
referencePointers to where information livesTOOLS.md / MEMORY.md"Brave API ~1000/mo free, usage tracked in memory/brave-search-usage.json"

Writing Format

Every feedback/project memory entry should follow:

**Rule/Fact:** [the thing itself]
**Why:** [reason — past incident, user preference, or constraint]
**How to apply:** [when/where this kicks in]

Key Principles

  1. Record success too — Only recording failures makes the agent overly cautious. "This approach worked well + why" is as valuable as "this broke + why"
  2. Include Why — Without Why, the agent can't judge edge cases and blindly follows rules
  3. Absolute dates only — Write "2026-03-31" not "today" or "yesterday". Memories must be interpretable after time passes
  4. Verify before trusting — "Memory says X exists" ≠ "X exists now". Check current state before acting on recalled memories

What NOT to Store

  • Code patterns, architecture, file structure (derivable from code)
  • Git history (git log is authoritative)
  • Debugging solutions (the fix is in the code, commit message has context)
  • Ephemeral task details or current conversation context
  • Content already in CLAUDE.md / AGENTS.md

Even if the user asks to save something from the exclusion list, ask what was *surprising* or *non-obvious* — that's the part worth keeping.

MEMORY.md Size Control

Hard limit: 200 lines / 25KB. When exceeded, run a Dream cycle.

Check with: wc -l MEMORY.md && wc -c MEMORY.md

Dream Cycle — 记忆整合流程

A periodic consolidation pass over memory files. Run during heartbeats or when MEMORY.md exceeds limits.

Trigger Gates (all three must pass)

  1. Time gate: ≥ 3 days since last Dream
  2. Session gate: ≥ 3 days of new daily notes since last Dream
  3. Lock gate: No other Dream in progress

Track state in memory/heartbeat-state.json:

{
  "lastDreamAt": "2026-03-31T15:40:00Z",
  "lastDreamResult": "pruned from 269 to 85 lines"
}

Four Phases

Phase 1 — Orient

  • Read MEMORY.md — understand current long-term memory landscape
  • Scan recent memory/YYYY-MM-DD.md files since last Dream
  • Note current MEMORY.md line count and byte size

Phase 2 — Gather

  • Extract entries worth keeping long-term from daily notes
  • Classify each as user / feedback / project / reference
  • Priority: important decisions, then lessons learned, then discoveries, then people info
  • Collect successes too, not just failures

Phase 3 — Consolidate

  • Write or update MEMORY.md entries in appropriate sections
  • Convert any relative dates to absolute dates
  • Delete contradicted facts (new info overrides old)
  • Merge near-duplicate entries
  • For feedback type: ensure each has Why + How to apply

Phase 4 — Prune

  • Enforce ≤ 200 lines / 25KB limit
  • Remove: completed projects, resolved issues, stale preferences
  • Replace verbose details with pointers: "详见 daily notes 2026-03-31"
  • Check for and resolve contradictions between entries

After Dream

  • Update lastDreamAt in heartbeat-state.json
  • Log what was done in today's daily notes: lines before → after, what was added/removed

Setup Guide — First-Time Configuration

To add this system to a new OpenClaw agent:

  1. Add the memory classification table and Dream procedure to AGENTS.md
  2. Add size limit header to MEMORY.md:
   > 📏 限制:≤ 200 行 / 25KB | 上次整理:YYYY-MM-DD
  1. Add lastDreamAt field to memory/heartbeat-state.json
  2. Add Dream trigger check to HEARTBEAT.md:
   - [ ] **记忆整理(Dream)**:检查是否满足三重门控,满足则执行四阶段整理
  1. Run an initial Dream cycle to establish baseline

Memory Drift Warning

Before acting on a recalled memory:

  • If memory names a file path → check the file exists
  • If memory names a function or config → grep for it
  • If memory is a state snapshot (activity log, architecture) → prefer git log or reading current code
  • If memory conflicts with current reality → trust what you see now, update the memory

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。

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按下载量换算1,150

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安装前确认

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