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auto-memory-curation自动记忆管理

Agent Skill

auto-memory-curation 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

10,169

周安装

428

GitHub Stars

公开资料未说明

下载量

3,561
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:auto-memory-curation(自动记忆管理)
来源仓库:https://github.com/init-v/auto-memory-curation
安装命令:
openclaw skills install auto-memory-curation
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 auto-memory-curation

简介

auto-memory-curation 用于查找、检索和筛选相关信息,适合在 OpenClaw 中根据关键词快速定位候选结果时使用。

  • 支持消息分析与信息存储,适用于对话内容归档。
  • 通过 clawhub 安装,命令为 openclaw skills install auto-memory-curation,需结合来源仓库进一步确认具体用法。
  • 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操作。
  • 当前功能描述基于原始 README,实际能力以官方文档为准。

SKILL.md

name
auto-memory-curation
description
Automatically analyzes messages for important information and stores it in the right memory files. Runs silently on every message. Filters noise, captures meaningful content. Use when you want passive memory building without manual "remember this" commands.
version
1.0.0

Auto Memory Curation Skill

Overview

Silently analyze every message for important information and store it appropriately. Reduces manual memory management while building rich context over time.

How It Works

Trigger

  • Runs on every message (silently)
  • No user activation needed

Analysis Pipeline

Step 1: Filter Noise Skip these message types:

  • Greetings (hi, hello, hey)
  • Thanks/thanking
  • Acknowledgments (ok, sure, yes, yeah)
  • Questions without context
  • Single word responses
  • Bot commands

Step 2: Categorize For non-noise messages, categorize:

CategoryWhat to look forStore in
FactNew info about user, projects, preferencesMEMORY.md
DecisionChoices made, conclusions reachedmemory/YYYY-MM-DD.md
PreferenceLikes, dislikes, style preferencesUSER.md
IdeaRandom thoughts, inspiration, conceptsmemory/topics/ideas.md
LearningLessons, insights, discoveriesmemory/topics/lessons.md
ProjectProject updates, progress, blockersmemory/projects/
GoalGoals, targets, milestonesmemory/topics/goals.md
ErrorMistakes, corrections to avoidmemory/topics/anti-patterns.md
CommitmentPromises I make, tasks to dotasks.md

Step 3: Extract & Store

  • Extract the key information
  • Add timestamp reference
  • Store in appropriate file
  • Use append mode (never overwrite)

Guidelines

What to Capture

  • New facts about Vini (name, preferences, goals)
  • Project updates or decisions
  • Ideas for future projects
  • Learning insights
  • Corrections (what doesn't work)
  • Commitments I make

What to SKIP

  • Passwords, secrets, API keys
  • Trivial acknowledgments
  • Basic confirmations
  • Questions I'm asking
  • Technical errors that are fixed

Quality Rules

  1. Be selective - Don't store everything
  2. Be concise - One sentence per memory
  3. Be contextual - Include enough info to understand later
  4. Be accurate - Don't paraphrase incorrectly
  5. Never duplicate - Check if already stored

Format

## [Category] - YYYY-MM-DD

- **[What]:** [Brief description]
  *Context:* [Why it matters or relevant message]

Examples

User says:

"I prefer concise messages, no filler words"

Stored in USER.md:

## Preference - 2026-03-06 - Communication: Prefers concise messages, no filler words

User says:

"Let's build a landing page for the consultancy inspired by Nexus AI"

Stored in memory/2026-03-06.md:

## Decision - 2026-03-06 - Consultancy: Will use Nexus AI style for landing page

User says:

"I realized I work better with visual examples first, then theory"

Stored in MEMORY.md:

## Learning - 2026-03-06 - Learning Style: Visual examples first, theory after

Testing

Periodically review stored memories to calibrate:

  • Am I capturing too much noise?
  • Am I missing important things?
  • Are categories correct?

Adjust based on quality of accumulated memories.

Override

User can disable or adjust this skill at any time by saying:

  • "Don't store that"
  • "Clear recent memories"
  • "Adjust memory curation"

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

71.44%
按下载量换算2,544

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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