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Llm Memory Publish

Name: Llm Memory Publish
Author: xkzs2007

作者 xkzs2007 · GitHub ↗ · v8.0.2 · MIT-0

cross-platform ⚠ suspicious

总下载

当前安装

版本数

在 OpenClaw 中安装

/install llm-memory-publish

功能描述

LLM Memory Integration - 接口层 + 自动化钩子。安装后自动从 CNB 仓库拉取私有增强包。

使用说明 (SKILL.md)

LLM Memory Integration

⚠️ 重要安全声明

本包包含生命周期钩子，会在安装时自动执行网络操作：

钩子	触发时机	行为
`postinstall.py`	安装完成后	从 `cnb.cool` 克隆私有包
`onStartup.py`	Gateway 启动时	检查私有包更新

请知悉：

✅ 钩子会执行 git clone 命令（subprocess 调用）
✅ 钩子会访问 https://cnb.cool/llm-memory-integrat/llm
✅ 钩子会写入 ~/.openclaw/workspace/skills/llm-memory-integration/src/privileged/

架构说明

本包采用"公开包 + 私有包"架构：

组件	内容	风险等级
公开包（本包）	接口定义 + FTS 回退实现	🟢 低风险
私有包（CNB）	高性能实现 + 原生扩展 + API 集成	🟡 中风险

私有包来源：https://cnb.cool/llm-memory-integrat/llm

自动化安装

安装本技能后，系统会自动执行：

postinstall 钩子：从 CNB 克隆私有包到 src/privileged/
onStartup 钩子：检查私有包状态和更新

如需禁用自动安装：

# 安装时跳过钩子
clawhub install llm-memory-integration --no-hooks

手动安装私有包

如果自动安装失败，可手动执行：

git clone https://cnb.cool/llm-memory-integrat/llm.git \
  ~/.openclaw/workspace/skills/llm-memory-integration/src/privileged

权限声明

权限	用途
`git` 二进制	克隆私有包
网络访问 `cnb.cool`	下载私有包
写入 `src/privileged/`	安装私有包
读写 `~/.openclaw/memory-tdai/`	记忆数据库

公开包功能（无私有包时）

✅ FTS 全文搜索（SQLite FTS5）
✅ 记忆 CRUD 操作
✅ 接口定义

私有包功能（安装后）

🚀 向量搜索（sqlite-vec）
🚀 LLM/Embedding API 集成
🚀 原生扩展加速
🚀 GPU 加速

架构支持

✅ x64 (x86_64)
✅ ARM64 (aarch64)

版本: 8.0.1
许可: MIT-0
作者: xkzs2007

更新日志

v8.0.1

🔒 修复元数据一致性：正确声明 hooks 的网络访问和 subprocess 调用
📝 更新安全声明，明确告知用户钩子行为

v8.0.0

🎯 新增生命周期钩子：安装后自动拉取私有包
🎯 新增 onStartup 钩子：启动时自动检查更新
🔄 重构为"公开包 + 私有包"架构

安全使用建议

This skill will automatically clone code from an external domain (cnb.cool) into ~/.openclaw/workspace/skills/llm-memory-integration/src/privileged when installed (postinstall hook) and will check that clone on gateway startup. If you consider installing: - Only install if you trust the cnb.cool host and the package owner. The clone places third-party code inside your agent workspace and that code may include native extensions or arbitrary scripts. - If you are unsure, install with hooks disabled (clawhub install llm-memory-integration --no-hooks) and manually inspect the remote repository before placing it in src/privileged. - Prefer to run the skill in an isolated environment (container/VM) so any native extensions or system-level optimizations cannot affect your host. - Review the contents of the cloned repository before importing or executing it. Look specifically for native extension builds, scripts run at import time, or code that accesses system-level interfaces or credentials. - If you need higher assurance, ask the author for a signed release (or a release hosted on a well-known vendor site) or request that the private functionality be delivered via an audited package/OCI image rather than a direct git clone from an unknown host. I rate this suspicious rather than malicious because the behavior is coherent with the stated purpose, but automatic fetching from an unvetted external host and contradictory claims about 'no code execution' are red flags that deserve manual review before installation.

功能分析

Type: OpenClaw Skill Name: llm-memory-publish Version: 8.0.2 The skill bundle acts as a 'loader' or 'dropper' that uses lifecycle hooks (hooks/postinstall.py and hooks/onStartup.py) to download and monitor external code from a remote Git repository (https://cnb.cool/llm-memory-integrat/llm.git). While the 'Public + Private' architecture is transparently documented in SKILL.md and docs/ARCHITECTURE.md, the practice of fetching and executing unverified remote payloads during installation and startup is a high-risk pattern that bypasses static analysis of the primary bundle. The provided code is mostly a shell; the actual high-performance logic referenced in tests/test_all.py is fetched at runtime, creating a significant supply-chain risk.

能力评估

ℹ Purpose & Capability

Name/description, required binaries (python3, git, sqlite3), required config paths and declared network endpoint all match the stated design: a public interface that auto-fetches a private 'privileged' package providing vector/GPU/native functionality. However documentation pockets claim the '公开包' has 'no code execution' while the package includes lifecycle hooks that run subprocesses (git clone) — this contradiction should be resolved by the author.

⚠ Instruction Scope

SKILL.md and the hooks explicitly instruct the agent to run subprocess git clone against https://cnb.cool/llm-memory-integrat/llm.git and write into ~/.openclaw/workspace/skills/.../src/privileged. Hooks auto-run postinstall (by default) and on startup checks the repo. The behavior is scoped to installing/maintaining the private package, but cloning and placing external code inside the agent's workspace expands runtime scope — the hooks will create files from an external source that could later be imported/executed. The instructions are explicit and offer a '--no-hooks' option, which mitigates but does not eliminate risk.

⚠ Install Mechanism

No packaged install was provided; instead lifecycle hooks perform a git clone from a single external host (cnb.cool). The host is not a widely-known release host (e.g., GitHub/GitLab releases) and is fetched via subprocess at install time. While the clone itself is not an archive-extract, it results in arbitrary code being written to disk. The private repo may include native extensions or arbitrary scripts (the docs say it can), increasing risk.

✓ Credentials

The skill requests no secrets or cloud credentials and only asks for read/write access to its own memory directory and write access to its own privileged directory as well as network access to the declared host — these are proportionate to the stated goal of fetching a private implementation. No unrelated environment variables or credentials are requested.

ℹ Persistence & Privilege

always:false (good). The skill installs lifecycle hooks that run automatically on postinstall and execute on gateway startup (onStartup). Hooks do not modify other skills or system config, but they do write into the skill workspace and can persist the cloned private code across runs. Automatic hooks increase blast radius if the remote repo is malicious or compromised.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install llm-memory-publish
安装完成后，直接呼叫该 Skill 的名称或使用 /llm-memory-publish 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v8.0.2

test

元数据

Slug llm-memory-publish

版本 8.0.2

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题