Skill flagged — suspicious patterns detected

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Mem0 Tech Tree Memory

v5.0.0

Manage and explore a tech tree of knowledge nodes with dependencies, unlock paths, synergies, tiers, and mastery progress tracking.

0· 97·0 current·0 all-time

Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for 371166758-qq/mem0-tech-tree.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Mem0 Tech Tree Memory" (371166758-qq/mem0-tech-tree) from ClawHub.
Skill page: https://clawhub.ai/371166758-qq/mem0-tech-tree
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install mem0-tech-tree

ClawHub CLI

Package manager switcher

npx clawhub@latest install mem0-tech-tree
Security Scan
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OpenClawOpenClaw
Suspicious
medium confidence
Purpose & Capability
Name/description, SKILL.md and mem0_skill.py align: a local tech-tree memory manager that reads/writes tree.json and provides store/retrieve/list/etc. However package.json declares an npm dependency (mem0ai) and Node metadata while the runtime is Python — this is disproportionate and unexplained.
Instruction Scope
SKILL.md only instructs running the included Python script (store/retrieve/tree/info/list/clear). The instructions describe local operations on the tech tree; they do not request external endpoints, credentials, or reading unrelated system paths. Based on the visible Python code, operations are local (load/save tree.json, tokenization, graph analysis).
Install Mechanism
There is no install spec (instruction-only), which is low risk. But package.json is present with an npm dependency (mem0ai). If someone runs npm install this could pull external code; that behavior is not required by the SKILL.md and is unexpected.
Credentials
The skill declares no required environment variables or credentials and the SKILL.md does not ask for any. The tree.json sample contains entries mentioning tools and configured services (OpenAI, OpenClaw, launchd) but these are content strings, not authorization requests. Still, the presence of those strings is informational and not a declared requirement.
Persistence & Privilege
Skill does not request always:true and is user-invocable only. It persists only to its own tree.json file (read/write in same directory). It does not request system-wide config changes or modify other skills.
What to consider before installing
This skill appears to implement a local tech-tree manager and the SKILL.md and Python code are largely coherent with that purpose. However: (1) Inspect the full mem0_skill.py before running — search for network, http, socket, subprocess, os.system, urllib, requests, or any hardcoded endpoints; the file was truncated in the package listing so verify there are no unexpected external calls. (2) Do not run npm install in this directory unless you trust the mem0ai package author — package.json lists an unrelated Node dependency that the Python script doesn't need. (3) Back up tree.json before using store/clear and run the script under a non-privileged user or in an isolated environment (container/VM) first. (4) If you plan to use it long-term, consider running it in a virtualenv and auditing third-party imports (jieba, etc.). If you want, I can scan the remainder of mem0_skill.py for network or subprocess usage if you paste the truncated portion.

Like a lobster shell, security has layers — review code before you run it.

aivk97brcfv6ce958nmdenhvh1hpx83pwbvcognitivevk97brcfv6ce958nmdenhvh1hpx83pwbvknowledge-graphvk97brcfv6ce958nmdenhvh1hpx83pwbvlatestvk97brcfv6ce958nmdenhvh1hpx83pwbvmemoryvk97brcfv6ce958nmdenhvh1hpx83pwbvtech-treevk97brcfv6ce958nmdenhvh1hpx83pwbv
97downloads
0stars
1versions
Updated 1mo ago
v5.0.0
MIT-0

Mem0 Tech Tree Memory System v5

科技树架构:知识节点有依赖关系、解锁路径、协同加成

核心概念

概念说明
🌳 节点 (Node)一条知识/技能/经验,有唯一ID
🔗 边 (Edge)节点间关系:dependency(前置依赖)、synergy(协同加成)、related(关联)
🌿 分支 (Branch)领域方向:技术/内容创作/学习/工作/生活
📊 层级 (Tier)T1(了解)→T5(创新),自动检测
🎯 状态 (State)⬜new → 🔘available → ✅unlocked → ⭐mastered

科技树如何工作

⬜ T1 [n0001] 了解Python基础语法
⬜ T2 [n0003] 熟悉playwright浏览器自动化
│  ⬜ T4 [n0005] 精通即梦AI自动化流程
⬜ T2 [n0006] 配置ffmpeg合成视频和音频
  • n0003(n0005的前置): 不会playwright就不可能做即梦自动化
  • T1→T4: 从入门到精通的进阶路径
  • 跨分支节点自动发现协同关系

用法

# 存储(自动检测分支、层级、类型)
python3 mem0_skill.py store "精通ComfyUI部署和模型训练"

# 检索(目录定位→范围搜索+依赖路径)
python3 mem0_skill.py retrieve "自动化发布视频"

# 查看科技树地图
python3 mem0_skill.py tree              # 全局
python3 mem0_skill.py tree 技术         # 指定分支

# 查看节点详情(依赖+解锁+协同)
python3 mem0_skill.py info n0005

# 查目录(O(1)关键词查找)
python3 mem0_skill.py catalog CDP

# 概览
python3 mem0_skill.py list

# 清除
python3 mem0_skill.py clear

检索流程

查询 → 查目录(关键词O(1)) → 定位节点 → 范围内评分 → 返回知识+依赖路径+协同

AI优势

  • 自动依赖发现: "学会playwright"自动成为"精通即梦自动化"的前置
  • 自动层级检测: "了解/学会/掌握/精通/创新" → T1-T5
  • 协同加成: 技术+内容创作交叉节点自动标记synergy
  • 进度系统: new→available→unlocked→mastered,高频访问节点自动升级
  • 完美回忆: 原始内容不篡改,每次检索强化但不修改

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