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Agent Skill · 研究检索

claude-memory(Claude 记忆)

claude-memory 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。

claude-memory 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;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 11,514。

总安装 11,198周安装 453GitHub Stars 公开资料未说明下载 3,515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
1.2.0

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:claude-memory(Claude 记忆)
来源仓库:https://github.com/kaiji-z/claude-memory
安装命令:
openclaw skills install claude-memory
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 claude-memory

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。

结构化内存管理系统,支持四类分类存储与分层归档。基于 MEMORY.md 与主题文件夹实现知识组织。适用于长期记忆保持与上下文追溯需求。需定期维护文件结构与元数据一致性。建议制定命名规范以提升检索效率。claude-memory 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 claude-memory;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

claude-memory 怎么安装?

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

claude-memory 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。

claude-memory 需要什么权限?

权限级别是只读。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。

claude-memory 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。

claude-memory 用的人多吗?

公开数据是累计安装 11,198 次,近一周安装 453 次,下载 3,515 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。

claude-memory 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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
claude-memory

Claude 记忆 · 本页

11,198—只读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
claude-memory
description
Structured memory system for OpenClaw agents. Provides a 4-type classification (user/feedback/project/reference), layered architecture (MEMORY.md + topics/ + feedback/ + daily notes), write rules, and heartbeat maintenance. Use when setting up a new agent's memory, improving an existing agent's memory system, or answering questions about memory management best practices. Triggers on phrases like "memory system", "memory setup", "记忆系统", "记忆管理", "setup memory", "improve memory", "memory architecture".

Claude Memory

A structured, layered memory architecture for OpenClaw agents. Uses a 4-type classification (user/feedback/project/reference) for organized memory, combined with semantic search and progressive disclosure for token-efficient retrieval.

Architecture

MEMORY.md (≤10KB, injected every turn via system prompt)
├── High-frequency info directly readable (inline)
├── Pointers to details in topics/
└── 4 sections: user | feedback → feedback.md | project | reference

memory/
├── feedback.md     # Corrections AND confirmations from human (MOST IMPORTANT)
├── YYYY-MM-DD.md   # Daily raw notes
└── topics/         # Low-frequency large content
    ├── agent-ids.md
    ├── known-issues.md
    └── ... (domain-specific)

4-Type Classification

TypePurposeExamples
userHuman's personal info, preferences, relationshipsIDs, timezone, family, privacy rules
feedbackCorrections AND confirmations from human"Check docs first", "That approach was right"
projectWork items, tasks, known issuesActive bugs, cron jobs, cleanup history
referenceTechnical resources, environmentRuntime config, connected services, security events

Setup

Run the init script to create the directory structure and template files:

python scripts/init_memory.py <workspace-path>

This creates:

  • MEMORY.md with empty section templates
  • memory/feedback.md with format guide
  • memory/topics/ directory
  • Instructions to update AGENTS.md memory rules

After running, edit MEMORY.md sections with actual content and update AGENTS.md to include the memory rules from references/agents-rules.md.

Write Rules

  1. Feedback immediately — When human corrects you, write to memory/feedback.md NOW. Not later, not "I'll remember", NOW.
  2. Record confirmations too — When human validates a non-obvious approach ("yes exactly", "keep doing that"), record it. If you only save corrections, you'll avoid past mistakes but drift away from approaches the human has already approved, becoming overly cautious.
  3. MEMORY.md ≤ 10KB — Hybrid format: inline high-frequency info for direct readability, pointers to topics/ for depth. If it exceeds 10KB, move lower-frequency content to topics/ — the content is preserved, just relocated.
  4. Read before answer — When MEMORY.md says "see topics/X.md", read that file first. Never answer from a one-line summary.
  5. Absolute dates only — Convert relative dates ("yesterday", "last week") to absolute dates (e.g., "2026-04-01") when writing. Memories should remain interpretable months later.
  6. Don't memorize what tools can look up — File paths, git history, code structure, current weather, live data.
  7. Don't memorize ephemeral state — In-progress work, temporary conversation context, things that resolve themselves.

What NOT to Save

  • Information tools can look up in real-time (weather, time, current stock prices)
  • Code patterns, architecture, file paths, project structure — derivable by reading the project
  • Git history, recent changes, who-changed-what — git log is authoritative
  • Debugging solutions or fix recipes — the fix is in the code; the commit has the context
  • Ephemeral task details: in-progress work, temporary state, current conversation context
  • Anything already documented in CLAUDE.md or equivalent project files

Verification Rules

Memories are long-term assets, not consumables. They don't expire. But some memories need verification before use:

  • Personal preferences, relationships, history events — Generally stable. No verification needed.
  • Technical state (service configs, bug status, installed versions) — Verify before acting on them. The world changes.
  • Before recommending based on memory — If the memory names a specific file, check it exists. If it names a function or flag, grep for it. "The memory says X exists" is not the same as "X exists now."

Feedback Format

Each feedback entry follows this structure. Two types: correction (don't do X) and confirmation (keep doing X).

### F###: Rule description (date)
- **Why**: Root cause or context
- **How to apply**: Concrete scenarios

### F###: Confirmed approach description (确认 date)
- **Context**: What was being worked on
- **Why**: What made this approach noteworthy or validated

Heartbeat Maintenance

Memory is an asset, not a consumable. Memories should not be deleted because they're old.

⚠️ Setup requirement: This 4-phase routine MUST be written into HEARTBEAT.md, NOT AGENTS.md. HEARTBEAT.md is injected only during heartbeat polls (token-efficient), while AGENTS.md is loaded every session (wastes token on every message). Write memory write rules in AGENTS.md, write this maintenance routine in HEARTBEAT.md.

During heartbeat polls, follow this 4-phase care routine:

Phase 1 — Catch-up (补漏)

  • Did the human correct or confirm something this session that isn't recorded in memory/feedback.md?
  • Are there significant events from this conversation not yet in daily notes?

Phase 2 — Consolidate (整合)

  • Review recent daily notes (last 1-3 days) for insights worth elevating to MEMORY.md or topics/.
  • Merge new signal into existing topic files rather than creating near-duplicates.
  • Daily notes that have been fully consolidated can be left in place as historical records — do NOT delete them.

Phase 3 — Verify (校验)

  • Pick 1-2 entries from MEMORY.md and spot-check if they're still accurate.
  • Focus on technical state entries (service configs, bug status, versions). Personal info and historical events don't need verification.
  • If a memory conflicts with current reality, update it — don't delete it, correct it.

Phase 4 — Tidy (整理)

  • If MEMORY.md exceeds 10KB, relocate lower-frequency content to topics/. The content is preserved, just moved to a more appropriate home.
  • Ensure topic files have a timestamp annotation: > Created: YYYY-MM-DD | Updated: YYYY-MM-DD
  • Add pointers in MEMORY.md for any newly created topic files.

Design Decisions

  • MEMORY.md as hybrid (content + pointers) — High-frequency info is directly readable for warmth and efficiency. Deep details live in topics/. Not a pure index — an agent's memory should feel like a person's, not a database.
  • Memories don't expire — Unlike a coding agent where project state changes fast, a daily assistant's memories are long-term assets. Old memories get relocated (to topics/) if MEMORY.md needs space, never deleted.
  • Feedback includes confirmations — Recording only corrections makes the agent overly cautious. Validated approaches ("yes, keep doing that") are just as important to remember.
  • Verification is selective — Personal preferences and history are stable facts. Only technical state (configs, versions, bug status) needs re-verification.
  • Feedback as separate file — Highest-priority memory. Must never be buried under daily notes.
  • Topics for depth — Low-frequency but detailed content lives in topics/ to keep MEMORY.md lean.
  • No auto-extraction — Requires OpenClaw runtime support. Currently manual via rules.

Advanced

For detailed AGENTS.md rules, see references/agents-rules.md. For troubleshooting, see references/encoding-notes.md.

平台分布

OpenClaw

70.31%
按下载量换算2,471

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。目前只有一个公开来源,建议同时核对源仓库。

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