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graph-memory-zero图形内存为零

Agent Skill

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。

总安装

3,164

周安装

128

GitHub Stars

公开资料未说明

下载量

993
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: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

简介

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。

SKILL.md

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.

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

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 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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