记忆蒸馏器

SKILL运维和基础设施
运维和基础设施 未标注权限 许可证不明确
安装这个技能

智能日志蒸馏系统,分类型差异化压缩,含溯源链与质量评估,4-8倍压缩比零关键事件损失。记忆蒸馏器是面向 AI Agent 日志的智能压缩系统,针对日志膨胀、关键信息丢失、压缩后难回溯、 不同

一句话选型结论

按资料字段整理

记忆蒸馏器是运维和基础设施类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类运维和基础设施 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install zcwl/memory-distiller
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

运维和基础设施
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方zcwl仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 enterprise
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791680590
正文体量6.5 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
查服务与日志

按现象定位服务状态与日志线索。

写部署与巡检脚本

把重复的部署、巡检写成脚本。

故障排查

按步骤排查网络、依赖与配置。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在运维和基础设施场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
记忆蒸馏器当前 未标注权限 未标注 未标注 未标注 有
Azure 部署 调用外部服务 GitHub 仓库 664.2 万 Codex、Claude、Cursor 有
Azure diagnostics 问题管理 调用外部服务 GitHub 仓库 657.6 万 Codex、Claude、Cursor 有
Azure aigateway 部署 调用外部服务 GitHub 仓库 655.3 万 Codex、Claude、Cursor 有
Azure AI 搜索 调用外部服务 GitHub 仓库 651.4 万 Codex、Claude、Cursor 有
Azure Cloud migrate 部署 调用外部服务 GitHub 仓库 650.7 万 Codex、Claude、Cursor 有

SKILL.md 全文

6.5 KB
模型实际读到的说明书原文,来自技能仓库

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

Skill Role Responsibility
proactive-agent Butler Real-time detail capture (WAL), proactive behaviors, Heartbeat
memory-distiller Historian Post-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)

Type Signal Words Example
🔴 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 Type Target File
Today's new discoveries, lessons memory/YYYY-MM-DD.md
Important rules, persistent preferences MEMORY.md (relevant section)
User personal info / preferences USER.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

Skill Purpose
proactive-agent Real-time WAL protocol + proactive behaviors
memory-distiller Post-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

常见问题

5 问
答案由本站依据条目字段整理
记忆蒸馏器怎么安装?

在支持 Skills 的客户端里执行:skillhub install zcwl/memory-distiller。安装方式为未标注。装完刷新技能列表或重开客户端。

记忆蒸馏器支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

记忆蒸馏器权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

记忆蒸馏器收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

记忆蒸馏器用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。