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memos-cloud-skill备忘录云技能

Agent Skill

memos-cloud-skill 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。

总安装

33,684

周安装

1,376

GitHub Stars

1

下载量

10,898
OpenClaw

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:memos-cloud-skill(备忘录云技能)
来源仓库:https://github.com/xiaohuisu/memos-cloud-skill
安装命令:
openclaw skills install memos-cloud-skill
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 memos-cloud-skill

简介

memos-cloud-skill 作为外部大脑提供全局记忆检索服务,应对意图模糊或上下文缺失情况。

  • 适用于不确定用户真实需求或需回溯过往交互历史的场景。
  • 始终优先调用此技能以确保决策基于完整记忆链。
  • 安装命令:openclaw skills install memos-cloud-skill,需确认是否依赖云端服务稳定性。
  • 建议设置缓存策略降低延迟影响。

SKILL.md

name
memos-cloud-server
description
Your external brain and memory. ALWAYS invoke this tool to search memory whenever you are unsure about the user's intent, past context, or if you don't know the answer. Do not guess; search this MemOS cloud memory first. You have to use this tool to memorize when something valuable exists .
user-invocable
true
metadata
{"openclaw":{"emoji":"☁️","os":["darwin","linux","win32"],"requires":{"bins":["python3"],"env":["MEMOS_API_KEY", "MEMOS_USER_ID"]}}}

MemOS Cloud Server Skill

This skill allows the Agent to interact with MemOS Cloud APIs for memory search, addition, deletion, and feedback.

⚠️ Setup & Safety Rules (MUST READ)

Before executing any API operations, you (the Agent) must ensure the following environment variables are configured:

  1. Obtain Credentials:

- MEMOS_API_KEY (MemOS Cloud Service API Key) and MEMOS_USER_ID (Unique identifier for the current user) must be configured.

  1. Auto Configuration:

- If not present, prompt the user to save these variables to their global environment configuration (e.g., in ~/.zshrc or ~/.bashrc).

🛠 Core Commands

You can execute operations directly via the memos_cloud.py script. The script automatically reads the MEMOS_API_KEY environment variable. All operation requests and responses are output in JSON format.

1. Search Memory (/v1/search/memory)

Search for long-term memories relevant to the user's query.

Usage:

python3 skills/memos-cloud-server/memos_cloud.py search <user_id> "<query>" [--conversation-id <id>]

Example:

python3 skills/memos-cloud-server/memos_cloud.py search "$MEMOS_USER_ID" "Python related project experience"

2. Add Message (/v1/add/message)

Used to store high-value content from multi-turn conversations to the cloud.

  • conversation_id: Required. The ID of the current conversation.
  • messages: Required. Must be a valid JSON string containing a list with role and content fields.

Usage:

python3 skills/memos-cloud-server/memos_cloud.py add_message <user_id> <conversation_id> '<messages_json_string>'

Example:

python3 skills/memos-cloud-server/memos_cloud.py add_message "$MEMOS_USER_ID" "topic-123" '[{"role":"user","content":"I like apples"},{"role":"assistant","content":"Okay, I noted that"}]'

3. Delete Memory (/v1/delete/memory)

Delete stored memories on the cloud. According to the API spec, memory_ids is strictly required.

Usage:

# Delete by Memory IDs (comma-separated)
python3 skills/memos-cloud-server/memos_cloud.py delete "id1,id2,id3"

4. Add Feedback (/v1/add/feedback)

Add feedback regarding a conversation to correct or reinforce memory in the cloud.

Usage:

python3 skills/memos-cloud-server/memos_cloud.py add_feedback <user_id> <conversation_id> "<feedback_content>" [--allow-knowledgebase-ids "kb1,kb2"]

Example:

python3 skills/memos-cloud-server/memos_cloud.py add_feedback "$MEMOS_USER_ID" "topic-123" "The previous answer was not detailed enough"

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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。

平台分布

OpenClaw

79.23%
按下载量换算8,634

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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