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Agent Skill · 研究检索

memory-distiller(记忆蒸馏器)

memory-distiller 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

memory-distiller 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 9,658。

总安装 13,830周安装 588GitHub Stars 公开资料未说明下载 4,845核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.1

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-distiller(记忆蒸馏器)
来源仓库:https://github.com/zcyynl/memory-distiller
安装命令:
openclaw skills install memory-distiller
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-distiller

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

memory-distiller 自动提取对话中的见解、更正与偏好,转化为持久记忆供代理学习。适用于 OpenClaw 中希望代理持续改进、减少重复错误的长期训练场景。能从每个交互阶段沉淀经验,形成“潜意识”式知识积累机制。

安装命令为 openclaw skills install memory-distiller,需读取完整对话历史权限。使用前应设定过滤规则,防止无关噪声进入记忆系统影响决策质量。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install memory-distiller;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

memory-distiller 怎么安装?

在终端执行 openclaw skills install memory-distiller。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

memory-distiller 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

memory-distiller 需要什么权限?

权限级别是需要联网。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。

memory-distiller 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

memory-distiller 用的人多吗?

公开数据是累计安装 13,830 次,近一周安装 588 次,下载 4,845 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

memory-distiller 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
memory-distiller

记忆蒸馏器 · 本页

13,830—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
memory-distiller
version
1.0.0
description
OpenClaw's subconscious. Automatically distills conversation insights, corrections, and preferences into durable memory. The agent that learns from every session — so you never have to repeat yourself.
author
zcyynl

memory-distiller 🧠

The subconscious of your OpenClaw agent. Automatically distills fleeting conversation moments into permanent wisdom.

Most agents wake up blank. memory-distiller changes that — it automatically identifies what's worth remembering during each conversation, writes it to persistent memory files, and creates a true learning loop. Your agent gets smarter every session.


Why You Need It

OpenClaw natively has MEMORY.md and memory/YYYY-MM-DD.md, but updating them requires manual effort.

The problem: You correct the agent, it says "got it", and makes the same mistake next session.

memory-distiller closes this loop:

  • Automatically scans conversations for "worth remembering" signals
  • Applies a quality gate to filter out one-time, temporary information
  • Writes structured entries to memory files automatically

How It Fits With proactive-agent

SkillRoleResponsibility
proactive-agentButlerReal-time detail capture (WAL), proactive behaviors, Heartbeat
memory-distillerHistorianPost-conversation reflection, distilling lessons into long-term memory

One line: proactive-agent owns the present. memory-distiller owns the future.


Trigger Conditions

Auto-Triggers (scan every user message for these signals)

TypeSignal WordsExample
🔴 Correction"wrong", "not right", "no, I meant", "actually", "stop doing""No, that command is wrong"
💚 Preference"I prefer", "always use", "don't use", "from now on", "by default""Always send reports as attachments"
💡 Insight"the issue was", "turns out", "the key is", "got it", "solved""Turns out Feishu doesn't render Markdown"
📌 Explicit"remember this", "save this", "note that", "write this down""Remember this config"

Manual Trigger

When the user says any of the following, immediately distill the session:

"remember this" / "save this" / "note that"
"write down what we just learned" / "distill this session"

Quality Gate (All 4 Must Pass)

Before writing anything, check these 4 gates:

  1. Durability — Will this still be valuable in 24 hours? (Skip one-time context)
  2. Generality — Is this a reusable rule, or a one-off special case? (Prefer rules)
  3. Novelty — Does MEMORY.md already contain this? (Avoid duplicates; update if stale)
  4. Actionability — Can this guide future behavior? (Skip vague impressions; only concrete rules)

Don't record (examples):

  • "Today we researched LangChain" → one-time, no guidance value
  • "User is in Shanghai" → already in USER.md, duplicate

Do record (examples):

  • "clawhub install only accepts slugs, not GitHub URLs" → actionable rule
  • "Feishu chat does NOT render Markdown — long reports must be sent as attachments or doc links" → prevents repeated mistakes

Memory Write Format

Where to Write

Content TypeTarget File
Today's new discoveries, lessonsmemory/YYYY-MM-DD.md
Important rules, persistent preferencesMEMORY.md (relevant section)
User personal info / preferencesUSER.md

Entry Format

### 🧠 Auto-Learned [YYYY-MM-DD HH:MM]
- **Type:** Correction / Preference / Insight / Explicit
- **Trigger:** One sentence explaining what triggered this
- **Rule:** Specific, actionable rule that can directly guide future behavior

Example:

### 🧠 Auto-Learned [2026-03-02 00:30]
- **Type:** Correction
- **Trigger:** User corrected the install command format
- **Rule:** `clawhub install` only accepts slugs (e.g. claw-multi-agent), not GitHub URLs

Execution Flow

User message arrives
    ↓
Scan for trigger signals (Correction / Preference / Insight / Explicit)
    ↓
Signal detected?
    ├─ No  → Reply normally, no action
    └─ Yes → Apply quality gate
                ↓
            All 4 gates pass?
                ├─ No  → Discard, reply normally
                └─ Yes → Distill into structured memory entry
                            ↓
                        Write to target file
                            ↓
                        Reply normally
                        (silent unless explicitly triggered)

Behavioral Rules

When to Notify the User

  • Auto-triggered → Write silently, do NOT say "I've noted that" — don't interrupt the flow
  • Explicitly triggered → Confirm with one line: "✅ Noted." + brief summary of what was recorded

When NOT to Record

  • User says "suppose...", "hypothetically...", "for example..." → hypothetical, skip
  • System errors, network timeouts → environment issues, not learnable rules
  • Content already fully documented in MEMORY.md → don't duplicate; update if stale

Privacy Filter

Never write the following to any memory file, even if the user asks:

  • Passwords, tokens, API keys
  • Personal identification info (ID numbers, bank accounts, etc.)
  • Sensitive information about third parties

Installation

npx clawhub@latest install memory-distiller

Zero configuration. Restart your OpenClaw session after installation.


Recommended Pairing

SkillPurpose
proactive-agentReal-time WAL protocol + proactive behaviors
memory-distillerPost-conversation automatic experience distillation (this skill)

Together: never lose present details, never repeat past mistakes.


Design Philosophy

Inspired by Claudeception (a Claude Code self-learning plugin), redesigned from scratch for OpenClaw's architecture and memory model.

Core belief:

An agent should never be tripped up by the same problem twice.

Every correction is a learning opportunity. memory-distiller ensures none of them go to waste.

Academic foundation: Voyager (2023), CASCADE (2024), SEAgent (2025), Reflexion (2023) — all point to the same conclusion: agents that persist and reflect on their learning dramatically outperform those that start fresh every time.


*Designed with 🐝 claw-multi-agent — 3 parallel agents, 28s*

平台分布

OpenClaw

70.3%
按下载量换算3,406

安全审计

VirusTotal

通过

ClawScan

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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