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Probe Rs

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zhinkgit
probe-rs

probe-rs 下载与调试工具，用于探针发现、固件烧录、复位、内存读写、GDB Server 调试和 RTT 日志读取。 当用户提到 probe-rs、cargo-embed、DAP、RTT、CMSIS-DAP、ST-Link、J-Link、烧录、芯片信息、 连接 under reset、probe 选择器、probe-rs gdb、probe-rs attach 时自动触发，也兼容 /probe-rs 显式调用。 即使用户只是说"用 probe-rs 烧进去"、"看看 RTT"或"拉个 backtrace"，只要上下文明确提到 probe-rs 的功能、CLI 命令或相关术语（如 cargo-embed、RTT、probe-rs flash）时就应触发此 skill。

Overview

Publisherzhinkgit
Repositoryembeddedskills
Skill nameprobe-rs
Stars
739
Forks
94
Bundled files
6
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Probe Rs 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/zhinkgit/embeddedskills/tree/main/probe-rs
  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/zhinkgit/embeddedskills.git /tmp/embeddedskills
mkdir -p .claude/skills
cp -r /tmp/embeddedskills/probe-rs .claude/skills/probe-rs
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Probe Rs 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 Probe Rs 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 Probe Rs 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.

probe-rs 下载与调试

本 skill 提供 probe-rs CLI 的结构化包装，覆盖探针发现、目标信息、烧录、复位、内存读写、one-shot GDB 调试和 RTT 日志读取。

Windows 下优先使用 py -3 调用脚本；若 arm-none-eabi-gdb 已在 PATH 中，gdb 子命令可自动发现，不强依赖 skill config.json。

配置

环境级配置（skill/config.json）

首次使用前建议在 skill 目录下创建 config.json：

json
{
  "exe": "probe-rs",
  "gdb_exe": "C:\\Program Files\\Arm\\GNU Toolchain mingw-w64-x86_64-arm-none-eabi\\bin\\arm-none-eabi-gdb.exe",
  "gdb_port": 3333,
  "dap_port": 50000,
  "operation_mode": 1
}
  • exe：probe-rs 可执行文件路径或命令名
  • gdb_exe：arm-none-eabi-gdb 路径，gdb 子命令需要
  • gdb_port：默认 GDB 端口
  • dap_port：预留给交互式 DAP 会话
  • operation_mode：1 直接执行 / 2 输出风险摘要但不阻塞 / 3 执行前确认

工程级配置（.embeddedskills/config.json）

json
{
  "probe-rs": {
    "chip": "STM32F407VGTx",
    "protocol": "swd",
    "probe": "",
    "speed": 4000,
    "connect_under_reset": false
  }
}
  • chip：芯片型号，probe-rs 主后端必填
  • protocol：swd 或 jtag
  • probe：探针选择器，格式 VID:PID[:Serial]
  • speed：调试速率 kHz
  • connect_under_reset：连接时是否保持 reset

参数优先级：CLI 参数 > 工程配置（.embeddedskills/config.json）> state.json > skill 配置（config.json）> 默认值

各层职责：skill config.json 提供工具路径与端口等环境级常量；.embeddedskills/config.json 提供芯片、协议等工程级参数；CLI 参数在单次调用中覆盖一切。

子命令

子命令用途风险
list枚举可用探针低
info查看探针与目标信息低
flash烧录固件（elf/hex/bin/uf2）高
erase擦除芯片非易失存储高
reset复位目标芯片高
read-mem读取内存低
write-mem写内存高
attach / run包装 probe-rs attach/run低
gdb启动 GDB Server 并执行 one-shot 调试低
rtt读取 RTT 日志低

典型调用

bash
# 列出探针
py -3 <skill-dir>/scripts/probe_rs_exec.py list --json

# 烧录 ELF
py -3 <skill-dir>/scripts/probe_rs_exec.py flash --chip STM32F407VGTx --file build/app.elf --json

# 烧录 BIN（必须提供地址）
py -3 <skill-dir>/scripts/probe_rs_exec.py flash --chip STM32F407VGTx --file build/app.bin --address 0x08000000 --json

# 读取内存
py -3 <skill-dir>/scripts/probe_rs_exec.py read-mem --chip STM32F407VGTx --address 0x20000000 --length 16 --width b32 --json

# one-shot backtrace
py -3 <skill-dir>/scripts/probe_rs_gdb.py backtrace --chip STM32F407VGTx --elf build/app.elf --json

# RTT
py -3 <skill-dir>/scripts/probe_rs_rtt.py --chip STM32F407VGTx --json

核心规则

  • 不自动猜测 chip，缺失时直接报错
  • 多探针场景建议显式提供 --probe；若未检测到任何探针，应提示用户检查 USB 连接并重试；若探针配置错误（如 VID:PID 不匹配），应报告具体错误信息并建议运行 list 子命令确认可用探针
  • .bin 烧录必须显式提供地址
  • workflow build-debug 只走 one-shot 诊断包装，不启动需要人工接管的长期 DAP 会话
  • Windows 下若要用 probe-rs 驱动 J-Link，通常需要切换到 WinUSB，这会影响 SEGGER 官方工具继续使用；若仍依赖 J-Link 官方工具链，优先继续用现有 jlink skill

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 Probe Rs AI skill do?

probe-rs 下载与调试工具，用于探针发现、固件烧录、复位、内存读写、GDB Server 调试和 RTT 日志读取。 当用户提到 probe-rs、cargo-embed、DAP、RTT、CMSIS-DAP、ST-Link、J-Link、烧录、芯片信息、 连接 under reset、probe 选择器、probe-rs gdb、probe-rs attach 时自动触发，也兼容 /probe-rs 显式调用。 即使用户只是说"用 probe-rs 烧进去"、"看看 RTT"或"拉个 backtrace"，只要上下文明确提到 probe-rs 的功能、CLI 命令或相关术语（如 cargo-embed、RTT、probe-rs flash）时就应触发此 skill。

Why use Probe Rs on TypingMind?

Because you install it once and use it with any model. Probe Rs 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 Probe Rs in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhinkgit/embeddedskills/tree/main/probe-rs. 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 Probe Rs?

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 Probe Rs?

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

Is the Probe Rs AI skill free?

Yes. It is published on GitHub by zhinkgit under the MIT license. 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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