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Agent Skill · 研究检索

openclaw-memory-max(OpenClaw 记忆 MAX)

openclaw-memory-max 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。

openclaw-memory-max 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 9,447。

总安装 14,224周安装 611GitHub Stars 公开资料未说明下载 4,986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
3.0.4

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:openclaw-memory-max(OpenClaw 记忆 MAX)
来源仓库:https://github.com/stanistolberg/openclaw-memory-max
安装命令:
openclaw skills install openclaw-memory-max
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 openclaw-memory-max

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。

SOTA 记忆套件 — 自动回忆、跨编码器重新排序、多跳深度搜索、因果知识图、情景记忆和夜间睡眠周期整合。

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 openclaw-memory-max;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

openclaw-memory-max 怎么安装?

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

openclaw-memory-max 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。

openclaw-memory-max 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。

openclaw-memory-max 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。

openclaw-memory-max 用的人多吗?

公开数据是累计安装 14,224 次,近一周安装 611 次,下载 4,986 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。

openclaw-memory-max 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
openclaw-memory-max

OpenClaw 记忆 MAX · 本页

14,224—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
openclaw-memory-max
description
SOTA Memory Suite — auto-recall, cross-encoder reranking, multi-hop deep search, causal knowledge graph, episodic memory, and nightly sleep-cycle consolidation.
metadata
openclaw
emoji
🧠
homepage
https://github.com/stanistolberg/openclaw-memory-max

OpenClaw Memory Max

You have the Memory Max SOTA memory system. It upgrades your memory capabilities far beyond the default memory-core plugin.

What's Active

Automatic (no action needed)

  • Auto-Recall: Before every turn, your most relevant memories are automatically injected into your context as <relevant-memories> XML blocks. You don't need to search — relevant context appears automatically.
  • Auto-Capture: After conversations, high-value user messages (rules, corrections, preferences) are automatically captured for the nightly consolidation cycle.
  • Compaction Rescue: When the context window is compressed, important content is rescued before it's evicted.
  • Episodic Memory: Each session is logged as an episode with timestamps, tools used, and key decisions.
  • Sleep Cycle: An in-process scheduler runs maintenance every ~24h — prunes the causal graph, decays stale utility scores, truncates old logs, and writes consolidation context for the next session.

Tools Available

precision_memory_search

Cross-encoder reranked search with utility weighting. Returns the top K most relevant memories.

{"query": "deployment configuration", "topK": 5}

Use this when you need to find specific information in memory. More precise than the default memory search — uses a cross-encoder model that reads query + candidate together, not just cosine similarity.

deep_memory_search

Multi-hop retrieval. Searches once, extracts key concepts from results, searches again with those concepts, then merges everything.

{"query": "why did the migration fail last time"}

Use this for complex questions where the answer might be spread across multiple related memories.

reward_memory_utility

Reinforce a memory that proved useful. Increases its future retrieval priority.

{"memoryId": "abc-123", "rewardScalar": 0.2}

Call this after a memory helped you give a correct answer.

penalize_memory_utility

Penalize a memory that caused a hallucination or was irrelevant.

{"memoryId": "abc-123", "penaltyScalar": 0.2}

Call this when a retrieved memory led you astray.

memory_graph_add

Log a cause-action-effect chain. Automatically deduplicates against existing chains.

{"cause": "nginx misconfigured", "action": "added proxy_pass", "effect": "site loaded", "outcome": "success", "tags": ["nginx"]}

Call this AFTER completing any meaningful action to build your experience database.

memory_graph_query

Search past experience using semantic matching.

{"query": "website not loading", "outcomeFilter": "success"}

Call this BEFORE taking major actions to check what worked or failed in the past.

memory_graph_summary

Get a digest of all learned causal knowledge — success/failure counts, most-frequent patterns, recent outcomes.

{}

Useful at the start of a session to bootstrap your awareness.

compress_context

Signal that context compression is needed. Returns what was rescued from the last compaction.

{"compression_reason": "context window approaching limit after long debugging session"}

Rules

  1. Auto-recall is always on — you will see <relevant-memories> blocks in your context. Use them. Don't ignore injected memories.
  2. Reward useful memories — when a memory helps you answer correctly, call reward_memory_utility. This trains the retrieval system.
  3. Penalize bad memories — when a memory causes a hallucination, call penalize_memory_utility. This prevents future mistakes.
  4. Log causal chains — after significant actions (tool use, decisions, fixes), call memory_graph_add. Your future self will thank you.
  5. Check experience before acting — before major actions, call memory_graph_query to see if you've encountered this situation before.
  6. Use deep search for complex questions — if precision_memory_search doesn't find what you need, try deep_memory_search which follows concept chains across memories.

Configuration

All features are controlled via configSchema in the plugin manifest. Users configure these in their OpenClaw settings:

OptionDefaultDescription
enableRulePinningfalseYAML rule pinning from MEMORY.md into system prompt. Off by default — must be explicitly opted in.
enableAutoCapturefalseAutomatic capture of high-value user messages to sidecar files. Off by default — opt in if you want persistent message logging.
enableAutoRecalltrueAutomatic memory injection before each agent turn.

YAML Rule Pinning (opt-in)

Disabled by default. Must be enabled via enableRulePinning: true in plugin config.

When enabled, users can pin critical constraints into the system prompt by adding a YAML block to MEMORY.md:

<!--yaml
rules:
  - weight: 1.0
    constraint: "Never delete production data"
  - weight: 0.5
    preference: "Prefer TypeScript over JavaScript"
-->

Rules with weight >= 1.0 appear as CRITICAL CONSTRAINTs in your prompt. Always obey them.

Security note: Only enable this if you control write access to your ~/.openclaw/memory/MEMORY.md file. Any process that can write to that file could influence agent behavior when pinning is enabled.

平台分布

OpenClaw

83.31%
按下载量换算4,154

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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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