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Agent Skill · 研究检索

graph-memory-zero(图形内存为零)

graph-memory-zero 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。

graph-memory-zero 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 28,755。

总安装 3,164周安装 128GitHub Stars 公开资料未说明下载 993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
1.0.2

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:graph-memory-zero(图形内存为零)
来源仓库:https://github.com/wangyangwjy/graph-memory-zero
安装命令:
openclaw skills install graph-memory-zero
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 graph-memory-zero

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。

优化 OpenClaw 图形内存与 mem0 对齐召回治理。提供生产环境内存状态总结与调优建议。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。通过 clawhub 安装,适用于大规模记忆系统的性能管理。操作前建议备份当前内存快照以防数据丢失。

graph-memory-zero 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 graph-memory-zero;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

graph-memory-zero 怎么安装?

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

graph-memory-zero 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。

graph-memory-zero 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。

graph-memory-zero 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。

graph-memory-zero 用的人多吗?

公开数据是累计安装 3,164 次,近一周安装 128 次,下载 993 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。

graph-memory-zero 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

  • 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

  • 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
graph-memory-zero

图形内存为零 · 本页

3,164—需要联网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

name
graph-memory-zero
description
Production playbook for OpenClaw graph-memory optimization with mem0-aligned recall governance. Use when users ask to (1) summarize current graph-memory status, (2) reproduce the same optimization effect on another workspace, (3) tune threshold/infer/memoryType/preferenceLexicon for precision vs recall, (4) troubleshoot recall quality drift, or (5) apply/rollback safe config patches under plugins.entries.graph-memory.config.

Graph Memory Zero

Mission

Deliver a reproducible graph-memory optimization outcome (not just a config diff):

  • stable recall behavior
  • explainable filtering semantics
  • safe rollout + rollback
  • observable runtime state

If user asks “达到你这套效果”, execute the full playbook below.

Load order (mandatory)

  1. references/current-baseline.md (known-good baseline)
  2. references/baseline-profiles.md (balanced/precision/recall profiles)
  3. references/verification-playbook.md (acceptance checks)
  4. references/troubleshooting.md (if any mismatch/failure)

When the user asks about install/download/distribution options, also load:

  • references/install-channels.md

Reproducible rollout workflow

Phase 0 — Snapshot and schema guard

  1. Run gateway.config.schema.lookup for:

- plugins.entries.graph-memory.config - plugins.entries.graph-memory.config.recallPolicy

  1. Run gateway.config.get and store:

- current config snapshot - baseHash

  1. Report: plugin enabled state, llm/embedding model, recall policy keys present.

Do not patch before confirming schema path exists.


Phase 1 — Normalize semantics (mem0-compatible)

Ensure these compatibility rules are explicitly explained in summary:

  • threshold is mem0-style alias; legacy minScore may still exist.
  • If both appear, effective threshold = max(threshold, minScore) (stricter wins).
  • infer is deterministic inference/expansion; no extra LLM call.
  • filters.memoryType supports fact|preference|task|event|all.
  • preferenceLexicon (versioned) has higher priority than legacy preferenceKeywords.

If any rule is not represented in runtime config, patch minimal fields only.


Phase 2 — Apply profile patch (minimal mutation)

Default profile is balanced unless user requests otherwise.

Use gateway.config.patch with smallest scoped patch under:

  • plugins.entries.graph-memory.config.recallPolicy

Balanced target (canonical):

  • threshold: 0.62
  • infer: true
  • filters.memoryType: all
  • preferenceLexicon.version: 2026-03-27.balance-v1
  • preferenceLexicon.enabled: true
  • preferenceLexicon.keywords: include EN+ZH preference words

If user asks for stronger precision or stronger recall, choose profile from references/baseline-profiles.md.


Phase 3 — Post-restart verification

After patch + restart, verify all below:

  1. Effective config re-read matches intended patch.
  2. gm_search debug details available (details.debug includes threshold/infer/filter summary).
  3. No schema/key regression (memoryType not dropped, lexicon keys intact).
  4. Query spot-checks pass (from verification playbook).

If any check fails, enter troubleshooting flow.


Phase 4 — Quality validation (must do before claiming success)

Run the query set in references/verification-playbook.md and compare:

  • preference-sensitive queries
  • task/event retrieval queries
  • mixed-language (CN/EN) preference terms

Success criteria (minimum):

  • relevant top hits improve or stay stable
  • off-topic hits do not increase materially
  • preference-related queries show better intent alignment

Do not claim “优化完成” without this phase.


Phase 5 — Rollback safety

Always keep rollback notes in output:

  • previous values (before)
  • target values (after)
  • one-step revert patch path

If regression is observed, rollback immediately to previous stable profile.

Failure handling

A) Local test execution fails

If extension tests fail locally but config intent is clear:

  1. Skip blocking local test path.
  2. Use controlled gateway.config.patch rollout.
  3. Run verification playbook.
  4. Keep explicit rollback entry.

B) PowerShell path / command failed

If errors indicate missing path or command failure:

  1. Validate path with Test-Path first.
  2. Confirm script/CLI location and permissions.
  3. Retry minimal command only after path is confirmed.

C) Version mismatch signals

If extension folder version and runtime installed version differ:

  • treat as metadata mismatch
  • continue config-level rollout, but report mismatch as release check item

Output contract (default reply structure)

Use this structure for user-facing summary:

  1. 当前状态:enabled / model / embedding / recallPolicy
  2. mem0 对齐语义:threshold-minScore、infer、memoryType、lexicon
  3. 本次变更:before → after(只列关键键)
  4. 验证结果:通过项 / 风险项 / 观测数据
  5. 下一步建议:继续调优或保持当前
  6. 回滚信息:可直接执行的 revert 说明

Keep answers concise-first, but never omit verification and rollback details.

Distribution guidance (when requested)

If user asks "how can others install this", provide at least 3 channels:

  1. ClawHub registry install (online)
  2. Offline package install (.skill as zip artifact)
  3. Source-folder install (copy skill folder into workspace skills/)

Always include:

  • required folder layout check (SKILL.md at skill root)
  • post-install reload step (openclaw gateway restart)
  • quick verification (skill appears in available skills and can be triggered)

Anti-patterns (forbid)

  • Large full-config overwrite when only recallPolicy needs change.
  • Declaring success without post-restart validation.
  • Ignoring threshold/minScore conflict resolution.
  • Omitting lexicon version in production summary.
  • Hiding test/verification gaps.

平台分布

OpenClaw

98.09%
按下载量换算974

安全审计

VirusTotal

可疑

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。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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