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claude-memoryClaude 记忆

Agent Skill

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。

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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: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

简介

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。

SKILL.md

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.

适合场景

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OpenClaw 用户查找和安装 Skill 时

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用户想查找某类 Agent Skill 时

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需要根据任务场景推荐可安装能力包时

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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。

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