A cross-platform, read-only, privacy-first Codex Skill that queries computer hardware, system, memory, storage, and GPU, producing an easy-to-understand Markdown report.

Stars

13

7-day growth

No data

Forks

0

Open issues

0

License

No data

Last updated

2026-07-03

AI repository intelligence
FR-AI / ANALYSIS

Why it is worth attention

It prioritizes user privacy by defaulting to minimal data exposure (only last 4 of serial, hidden username), uses only Python standard library, and works across macOS/Windows/Linux without requiring third-party packages.

Who it is for

  • Codex users who want to query their system config
  • Developers needing a quick hardware overview
  • Privacy-conscious users who want local-only inspection
  • IT support staff for basic system diagnostics

Use cases

  • Check computer specs before buying new software
  • Monitor disk space usage and cleanup needs
  • Assess whether the machine is suitable for programming or local AI
  • Quickly gather system info for troubleshooting

Strengths

  • Runs on all three major desktop OSes (macOS, Windows, Linux)
  • No third-party dependencies; pure Python standard library
  • Privacy-first: read-only operations, sensitive data masked by default
  • Generates clear Markdown reports suitable for non-technical users

Considerations

  • Hardware and OS variations may cause some fields to be unavailable (e.g., GPU info on Linux depends on driver support)
  • Requires Python 3.9+ installed on the system
  • Not a full system profiler; limited to predefined hardware and OS categories

README quick start

一个跨平台、只读、隐私优先的 Codex Skill。
查询电脑硬件、系统、内存、存储与显卡,并生成普通用户也能轻松看懂的 Markdown 报告。


✨ 它能做什么

能力macOSWindowsLinux输出示例
品牌、型号、设备名称Apple / MacBook Air
操作系统、版本、架构macOS 26 / arm64
CPU 型号、核心与线程Apple M 系列 / 10 核
内存总量、已用、可用32 GB / 约 22 GB / 约 10 GB
磁盘容量与使用率460.4 GB / 89%
显卡与显存视驱动支持Apple GPU / 共享内存
序列号隐私保护****-A1B2

[!NOTE] 不同系统、设备固件和权限环境能提供的信息不完全相同。无法读取的字段会明确显示“当前环境无法获取”,不会猜测或编造。

🧭 工作流程

flowchart LR
    A[用户询问本机配置] --> B{识别操作系统}
    B -->|macOS| C[只读系统查询]
    B -->|Windows| D[PowerShell CIM 查询]
    B -->|Linux| E[/proc · /sys · 只读命令]
    C --> F[统一 JSON]
    D --> F
    E --> F
    F --> G[敏感字段脱敏]
    G --> H[Markdown 配置报告]

🚀 快速开始

作为 Codex Skill 安装

将仓库克隆到 Codex 的 skills 目录:

git clone https://github.com/lxj960202-jerry/system-info-skill.git "${CODEX_HOME:-$HOME/.codex}/skills/system-info-skill"

然后可以这样提问:

使用 $system-info-skill 查询我的电脑配置。

也可以直接用自然语言:

我的电脑配置是什么?
帮我看一下磁盘空间还剩多少。
只看内存和 CPU。
我的电脑适合编程和本地 AI 吗?

直接运行查询脚本

无需安装第三方 Python 包:

python3 scripts/system_info.py --pretty

只查询某一部分:

python3 scripts/system_info.py --section storage --pretty
python3 scripts/system_info.py --section cpu --pretty
python3 scripts/system_info.py --section memory --pretty

Windows 也可以使用:

py -3 scripts/system_info.py --pretty

🖥️ 报告长什么样

# 本机配置报告

## 1. 设备信息
- 品牌:Apple
- 型号:MacBook Air
- 设备名称:用户的 MacBook Air
- 序列号:****-A1B2(已脱敏,仅后 4 位)

## 3. 处理器信息
- CPU 型号:Apple M 系列
- CPU 核心数:10
- CPU 线程数:10

## 5. 存储信息
| 磁盘/分区 | 总容量 | 已用 | 可用 | 使用率 | 挂载点 |
|---|---:|---:|---:|---:|---|
| 主数据分区 | 460.4 GB | 386.1 GB | 50.4 GB | 89% | /Data |

## 7. 简单解读
这台电脑适合日常办公、编程和常规创作任务。
主数据分区使用率较高,建议近期清理大型缓存或不再使用的文件。

示例全部使用虚构或泛化数据,不包含真实设备标识。

🎛️ 命令参数

参数作用
--section all查询全部信息,默认值
--section device只查询设备信息
--section system只查询系统信息
--section cpu只查询处理器
--section memory只查询内存
--section storage只查询存储
--section gpu只查询显卡
--pretty缩进格式化 JSON
--show-full-serial显示完整序列号,仅在用户明确要求后使用
--show-full-username显示完整用户名,仅在用户

Description

跨平台、只读、隐私优先的本机配置查询 Codex Skill

Related repositories

Similar projects matched by category, topics, and programming language.

lopopolo
Featured
lopopolo GitHub avatar

harness-engineering

Harness Engineering is a methodology for improving coding agent outputs by carefully crafting the environment around them—providing curated context, tools, and executable constraints that encode an organization’s nonfunctional requirements and cumulative lessons.

AI & Machine LearningAI Agents
2,390
slvDev
Featured
slvDev GitHub avatar

esp32-ai

A 28.9 million parameter language model runs on an $8 ESP32-S3 microcontroller entirely on-device, generating simple stories at about 9.5 tokens per second.

AI & Machine LearningLarge Language Models
1,960
littledivy
Featured
littledivy GitHub avatar

mimic

mimic captures traffic from any iOS or web app and automatically generates a Python client library that lets you call the app's API like a regular library.

AI & Machine Learning
1,482