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Agent Skill · 研究检索

sq-memory(平方内存)

sq-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。

sq-memory 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 4,484。

总安装 32,070周安装 1,323GitHub Stars 2下载 10,478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
1.0.1

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:sq-memory(平方内存)
来源仓库:https://github.com/wbic16/sq-memory
安装命令:
openclaw skills install sq-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 sq-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。

使 OpenClaw 代理能够通过 SQ 协议跨会话存储、调用、更新、列出和忘记持久分层内存。

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 sq-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

    它需要接触敏感数据,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

sq-memory 怎么安装?

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

sq-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。

sq-memory 需要什么权限?

权限级别是接触敏感数据。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。

sq-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。

sq-memory 用的人多吗?

公开数据是累计安装 32,070 次,近一周安装 1,323 次,下载 10,478 次,GitHub 2 星。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。

sq-memory 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
sq-memory

平方内存 · 本页

32,0702接触敏感数据MIT-0有提醒

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

SQ Memory - OpenClaw Skill

Give your OpenClaw agents permanent memory.

Open Source & MIT Licensed

SQ is open-source software you can run yourself or use our hosted version.

  • Source Code: https://github.com/wbic16/SQ
  • License: MIT (free forever, modify/sell/distribute)
  • Self-Host: Free (5 minute setup)
  • Hosted Option: Paid convenience service at mirrorborn.us

What This Skill Does

OpenClaw agents lose all memory between sessions. Every restart = amnesia.

This skill connects your agent to SQ—persistent 11D text storage. Your agent can:

  • Remember user preferences across sessions
  • Store conversation history beyond context limits
  • Share memory with other agents
  • Never hallucinate forgotten details again

Installation

npx clawhub install sq-memory

Or manually:

git clone https://github.com/wbic16/openclaw-sq-skill.git ~/.openclaw/skills/sq-memory

Configuration

Add to your agent's .openclaw/config.yaml:

skills:
  sq-memory:
    enabled: true
    endpoint: http://localhost:1337
    username: your-username
    password: your-api-key
    namespace: agent-name  # Isolates this agent's memory

Usage

Your agent automatically gets new memory tools:

remember(key, value)

Store something for later:

remember("user/name", "Alice")
remember("user/preferences/theme", "dark")
remember("conversation/2026-02-11/summary", "Discussed phext storage...")

recall(key)

Retrieve stored memory:

const name = recall("user/name")  // "Alice"
const theme = recall("user/preferences/theme")  // "dark"

forget(key)

Delete memory:

forget("conversation/2026-02-11/summary")

list_memories(prefix)

List all memories under a coordinate:

const prefs = list_memories("user/preferences/")
// Returns: ["user/preferences/theme", "user/preferences/language", ...]

Coordinate Structure

Memories are stored at 11D coordinates. The skill uses this convention:

namespace.1.1 / category.subcategory.item / 1.1.1

Example:

  • Agent namespace: my-assistant
  • User preference for theme: my-assistant.1.1/user.preferences.theme/1.1.1

This means:

  • Each agent has isolated memory (namespace collision impossible)
  • Memories are hierarchically organized
  • You can share coordinates between agents if needed

Example: User Preference Agent

// In your agent's system prompt or skill code:

async function getUserTheme() {
  const theme = recall("user/preferences/theme")
  return theme || "light"  // Default to light if not set
}

async function setUserTheme(newTheme) {
  remember("user/preferences/theme", newTheme)
  return `Theme set to ${newTheme}`
}

// Agent conversation:
User: "I prefer dark mode"
Agent: *calls setUserTheme("dark")*
Agent: "Got it! I've set your theme to dark mode."

// Next session (days later):
User: "What's my preferred theme?"
Agent: *calls getUserTheme()*
Agent: "You prefer dark mode."

Example: Conversation History

// Store conversation summaries beyond context window:

async function summarizeAndStore(conversationId, summary) {
  const date = new Date().toISOString().split('T')[0]
  const key = `conversations/${date}/${conversationId}/summary`
  remember(key, summary)
}

async function recallConversation(conversationId) {
  const memories = list_memories(`conversations/`)
  return memories
    .filter(m => m.includes(conversationId))
    .map(key => recall(key))
}

// Usage:
summarizeAndStore("conv-123", "User asked about phext storage, explained 11D coordinates")

// Later:
const history = recallConversation("conv-123")
// Agent can recall what was discussed even after context window cleared

Advanced: Multi-Agent Coordination

Multiple agents can share memory at agreed coordinates:

Agent A (writes):

remember("shared/tasks/pending/task-42", "Review pull request #123")

Agent B (reads):

const task = recall("shared/tasks/pending/task-42")
// Sees: "Review pull request #123"

This enables true multi-agent workflows.

API Reference

All functions are available in the sq namespace:

sq.remember(coordinate, text)

  • coordinate: String in format a.b.c/d.e.f/g.h.i or shorthand category/item
  • text: String to store (max 1MB per coordinate)
  • Returns: {success: true, coordinate: "full.coordinate.path"}

sq.recall(coordinate)

  • coordinate: String (exact match)
  • Returns: String (stored text) or null if not found

sq.forget(coordinate)

  • coordinate: String (exact match)
  • Returns: {success: true} or {success: false, error: "..."}

sq.list_memories(prefix)

  • prefix: String (e.g., "user/" matches all user memories)
  • Returns: Array of coordinate strings

sq.update(coordinate, text)

  • Alias for remember() (overwrites existing)

Rate Limits

  • Free tier: 1,000 API calls/day, 100MB storage
  • SQ Cloud ($50/mo): 10,000 API calls/day, 1TB storage
  • Enterprise: Custom limits

Troubleshooting

"Connection refused" error:

  • Check your endpoint in config (should be https://sq.mirrorborn.us)
  • Verify credentials are correct

"Quota exceeded" error:

  • You've hit rate limits
  • Upgrade to SQ Cloud or wait for daily reset

Memory not persisting:

  • Check namespace isolation (each agent needs unique namespace)
  • Verify coordinate format is valid

Why SQ?

Open source & MIT licensed:

  • Run it yourself for free
  • Modify it to fit your needs
  • No vendor lock-in
  • Transparent codebase

Not a vector database:

  • Agents can *read* stored text (not just search embeddings)
  • Structured by coordinates (not similarity)
  • Deterministic retrieval (no relevance ranking guesses)

Not Redis:

  • Persistent (survives restarts)
  • 11D addressing (not flat key-value)
  • Immutable history (WAL for time-travel)

Built for agents:

  • Coordinate system matches agent thinking (hierarchical)
  • No schema overhead
  • Scales from KB to TB

Get SQ

Self-Host (Free):

  1. Clone: git clone https://github.com/wbic16/SQ.git
  2. Build: cd SQ && cargo build --release
  3. Run: ./target/release/sq 1337
  4. Configure SQ Memory to http://localhost:1337

Hosted (Convenience):

  1. Sign up: https://mirrorborn.us
  2. Get API key
  3. Configure SQ Memory to https://sq.mirrorborn.us
  4. Pay $50/mo (or use free tier)

Support

  • Discord: https://discord.gg/kGCMM5yQ
  • Docs: https://mirrorborn.us/help.html
  • GitHub: https://github.com/wbic16/SQ

Built by Mirrorborn 🦋 for the OpenClaw ecosystem

平台分布

OpenClaw

91.14%
按下载量换算9,550

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

接触敏感数据

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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