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memory-reflect记忆反映

Agent Skill

memory-reflect 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。

总安装

10,128

周安装

422

GitHub Stars

公开资料未说明

下载量

3,376
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-reflect(记忆反映)
来源仓库:https://github.com/phernandez/memory-reflect
安装命令:
openclaw skills install memory-reflect
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-reflect

简介

memory-reflect 提供睡眠时对近期对话和笔记的反思功能,提取见解并入长期记忆。

  • 适用于需要巩固日常知识、回顾历史对话或增强上下文理解的任务场景。
  • 自动整合短期记忆为持久化知识,支持跨会话记忆检索。
  • 安装命令:openclaw skills install memory-reflect,需确认是否启用本地存储或日志读取权限。
  • 注意其依赖 cro 触发机制,应核实具体调用方式与系统兼容性。

SKILL.md

name
memory-reflect
description
Sleep-time memory reflection: review recent conversations and daily notes, extract insights, and consolidate into long-term memory. Use when triggered by cron, heartbeat, or explicit request to reflect on recent activity. Runs as background processing to improve memory quality over time.

Memory Reflect

Review recent activity and consolidate valuable insights into long-term memory.

Inspired by sleep-time compute — the idea that memory formation happens best *between* active sessions, not during them.

When to Run

  • Cron/heartbeat: Schedule as a periodic background task (recommended: 1-2x daily)
  • On demand: User asks to reflect, consolidate, or review recent memory
  • Post-compaction: After context window compaction events

Process

1. Gather Recent Material

Find what changed recently, then read the relevant files:

# Find recently modified notes — use json format for the complete list
# (text format truncates to ~5 items in the summary)
recent_activity(timeframe="2d", output_format="json")

# Read specific daily notes
read_note(identifier="memory/2026-02-27")
read_note(identifier="memory/2026-02-26")

# Check active tasks
search_notes(note_types=["task"], status="active")

2. Evaluate What Matters

For each piece of information, ask:

  • Is this a decision that affects future work? → Keep
  • Is this a lesson learned or mistake to avoid? → Keep
  • Is this a preference or working style insight? → Keep
  • Is this a relationship detail (who does what, contact info)? → Keep
  • Is this transient (weather checked, heartbeat ran, routine task)? → Skip
  • Is this already captured in MEMORY.md or another long-term file? → Skip

3. Update Long-Term Memory

Write consolidated insights to MEMORY.md following its existing structure:

  • Add new sections or update existing ones
  • Use concise, factual language
  • Include dates for temporal context
  • Remove or update outdated entries that the new information supersedes

4. Log the Reflection

Append a brief entry to today's daily note:

## Reflection (HH:MM)
- Reviewed: [list of files reviewed]
- Added to MEMORY.md: [brief summary of what was consolidated]
- Removed/updated: [anything cleaned up]

Guidelines

  • Be selective. The goal is distillation, not duplication. MEMORY.md should be curated wisdom, not a copy of daily notes.
  • Preserve voice. If the agent has a personality/soul file, reflections should match that voice.
  • Don't delete daily notes. They're the raw record. Reflection extracts from them; it doesn't replace them.
  • Merge, don't append. If MEMORY.md already has a section about a topic, update it in place rather than adding a duplicate entry.
  • Flag uncertainty. If something seems important but you're not sure, add it with a note like "(needs confirmation)" rather than skipping it entirely.
  • Restructure over time. If MEMORY.md is a chronological dump, restructure it into topical sections during reflection. Curated knowledge > raw logs.
  • Check for filesystem issues. Look for recursive nesting (memory/memory/memory/...), orphaned files, or bloat while gathering material.

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

75.82%
按下载量换算2,560

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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。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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