Analyze your AI conversations and generate a six-dimension ability radar chart
Project description
AI Checkup
你的 AI 使用体检报告 — 一行命令,生成六维能力雷达图
导出你的 AI 对话(或让工具自动扫描),跑一条命令,拿到六维能力评分:
| 维度 | 检测逻辑 | 信号强度 |
|---|---|---|
| 提问力 Questioning | 问题层级分布 (L1执行→L4元认知) | 强 |
| 判断力 Judgment | 方向设定 / 评价 / 否定修正信号 | 强 |
| 动手力 Hands-on | 输出型关键词占比 | 强 |
| 深思力 Deep Thinking | L3思维 + 反思型对话占比 | 弱 |
| 践行力 Execution | 每周活跃天数 | 弱 |
| 叙事力 Narrative | 写作 / 叙事型对话占比 | 弱 |
快速开始
方式一:AI CLI Skill(零摩擦)
Claude Code 用户,把 skill 文件复制到本地即可使用 /checkup:
cp skill/checkup.md ~/.claude/commands/checkup.md
然后在 Claude Code 中输入:
/checkup
自动扫描 ~/.claude 数据,生成报告。
Codex 用户,把 skill 文件复制到 codex skills 目录:
mkdir -p ~/.codex/skills/ai-checkup
cp skill/checkup.md ~/.codex/skills/ai-checkup/SKILL.md
方式二:命令行
# 自动扫描本地 AI 数据(Claude Code / Codex / Gemini CLI)
uvx ai-checkup
# 指定数据源
uvx ai-checkup ~/Downloads/conversations.json
# 内置演示
uvx ai-checkup --mock
也可以用 pip 安装:
pip install ai-checkup
ai-checkup --auto
支持的平台
| 平台 | 自动发现 | 手动导入 | 数据位置 |
|---|---|---|---|
| Claude Code | ✅ | ✅ | ~/.claude/projects/*/sessions-index.json |
| Codex (OpenAI) | ✅ | ✅ | ~/.codex/sessions/**/*.jsonl |
| Gemini CLI | ✅ | ✅ | ~/.gemini/tmp/*/chats/session-*.json |
| ChatGPT | — | ✅ | 设置 → 数据控制 → 导出数据 → conversations.json |
| Gemini Web | — | ✅ | Chrome 插件导出 JSON |
| 通用 CSV | — | ✅ | 自行整理为 conversation_id,role,content,timestamp |
各平台导出说明
ChatGPT:Settings → Data controls → Export data → 等邮件 → 解压 → conversations.json
Claude Web:暂不支持网页版导出。Claude Code 用户数据自动扫描。
Gemini Web:安装 Gemini Chat Exporter Chrome 插件 → 导出 JSON
通用 CSV:手动整理成 4 列格式:
conversation_id,role,content,timestamp
conv001,user,帮我写一个函数,2026-01-15T10:00:00
conv001,assistant,好的...,2026-01-15T10:01:00
输出文件
ai_checkup_report/
├── radar.png # 六维雷达图
├── report.md # 分析报告(评分 + 趋势 + 洞察)
└── data.json # 机器可读的原始数据
六维能力说明
强信号(基于用户提问的文本分析,信噪比高):
- 提问力:你的问题是执行型("帮我做X")还是探究型("为什么")还是元认知型("复盘/框架")?高阶问题占比越高,得分越高。
- 判断力:你是否在对话中展现主动判断——设定方向("先做A再做B")、评价("哪个好")、否定("不对")?
- 动手力:你的对话中有多少在产出实际成果——代码、部署、创建、修复?
弱信号(间接推断,仅供参考):
- 深思力:是否有反思型对话(复盘、总结教训)和深层探因(为什么/本质)?
- 践行力:每周有几天在用 AI?连续性反映习惯强度。
- 叙事力:是否在用 AI 写文章、文档、说明?将经验转化为表达的能力。
开发
git clone https://github.com/TerryFYL/ai-checkup.git
cd ai-checkup
python3 -m venv .venv && source .venv/bin/activate
pip install -e . pytest
pytest tests/ -v
背景
本工具源自《AI 时代什么不变》系列番外篇。六维能力框架的理论基础和设计思路详见文章。
License
MIT
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