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Can Debug

Community
LeoKemp223
can-debug

当需要调试 CAN 总线通信时使用,支持通过 USB-CAN 适配器监听、发送 CAN 帧和扫描节点。

Overview

PublisherLeoKemp223
Repositoryembed-ai-tool
Skill namecan-debug
Stars
995
Forks
168
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LeoKemp223 on GitHub. Read the source before you install it.

Installation

Install the Can Debug AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LeoKemp223/embed-ai-tool/tree/master/skills/can-debug
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LeoKemp223/embed-ai-tool.git /tmp/embed-ai-tool
mkdir -p .claude/skills
cp -r /tmp/embed-ai-tool/skills/can-debug .claude/skills/can-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Can Debug in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Can Debug on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Can Debug is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CAN 总线调试

适用场景

  • 嵌入式设备实现了 CAN 通信,需要验证收发是否正常。
  • 需要监听 CAN 总线上的所有帧或过滤特定 ID。
  • 需要向 CAN 总线发送测试帧并等待响应。
  • 需要扫描总线上的活跃节点。

必要输入

  • CAN 接口类型(pcan、kvaser、slcan、socketcan、virtual 等)。
  • 通道名(取决于接口类型,如 PCAN_USBBUS1、COM3、can0)。
  • 波特率(默认 500000)。

依赖

  • python-can(pip install python-can)
  • 对应适配器的驱动(如 PCAN 需要 PCAN-Basic API)

执行步骤

  1. 先阅读 references/usage.md,确认操作参数。
  2. 探测环境:
    bash
    python scripts/can_tool.py --detect
  3. 根据需求执行操作:
    bash
    # 监听总线
    python scripts/can_tool.py --interface pcan --channel PCAN_USBBUS1 --listen --duration 10
    
    # 发送帧
    python scripts/can_tool.py --interface pcan --channel PCAN_USBBUS1 --send --id 0x123 --data 01,02,03
    
    # 扫描节点
    python scripts/can_tool.py --interface pcan --channel PCAN_USBBUS1 --scan --scan-range 0x001-0x0FF

失败分流

  • connection-failure:适配器未连接或驱动未安装。
  • bus-error:CAN 总线错误(如未接终端电阻)。
  • timeout:发送后无响应。

输出约定

示例输出格式:

结果: ✅ 监听完成,收到 15 帧
  连接: pcan PCAN_USBBUS1 500000

  [14:30:01] 0x123  [8]  01 02 03 04 05 06 07 08
  [14:30:01] 0x456  [4]  AA BB CC DD

交接关系

  • 从 build-keil / build-platformio 烧录固件后,用此 skill 验证 CAN 通信。
  • 与 serial-monitor 互补:serial-monitor 查看串口调试输出,can-debug 进行 CAN 协议级调试。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Can Debug AI skill do?

当需要调试 CAN 总线通信时使用,支持通过 USB-CAN 适配器监听、发送 CAN 帧和扫描节点。

Why use Can Debug on TypingMind?

Because you install it once and use it with any model. Can Debug is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Can Debug in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeoKemp223/embed-ai-tool/tree/master/skills/can-debug. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Can Debug?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Can Debug?

As many as you like. As long as a model supports skills, you can use Can Debug with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Can Debug AI skill free?

It is published on GitHub by LeoKemp223. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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