Jlink logo

Jlink

Community
zhinkgit
jlink

J-Link 下载与在线调试工具,用于探测设备、烧录固件、读写内存、查看寄存器、复位目标、读取 RTT/SWO 日志, 以及在线调试(暂停/恢复/单步/断点运行/调用栈/变量查看)。 当用户提到 J-Link、JLink、RTT、烧录固件、写内存、读内存、寄存器查看、目标复位、探针连通性检查、 在线调试、单步、断点、调用栈时自动触发,也兼容 /jlink 显式调用。 即使用户只是说"烧录一下"、"看看 RTT 输出"或"调试一下",只要上下文涉及 J-Link 探针就应触发此 skill。

Overview

Publisherzhinkgit
Repositoryembeddedskills
Skill namejlink
Stars
739
Forks
94
Bundled files
11
LicenseMIT
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.

  • 11 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 Jlink 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/jlink
  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/jlink .claude/skills/jlink
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jlink 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 Jlink 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 Jlink 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.

J-Link 下载与在线调试

本 skill 提供 J-Link 探针的设备探测、固件烧录、内存读写、寄存器查看、目标复位、RTT 日志读取,以及轻量在线调试和 GDB 源码级调试能力。

配置

环境级配置(skill/config.json)

skill 目录下的 config.json 包含环境级配置(工具路径、端口号等),首次使用前确认 exe 路径正确:

json
{
  "exe": "C:\\Program Files\\SEGGER\\JLink\\JLink.exe",
  "gdbserver_exe": "C:\\Program Files\\SEGGER\\JLink\\JLinkGDBServerCL.exe",
  "rtt_exe": "C:\\Program Files\\SEGGER\\JLink\\JLinkRTTClient.exe",
  "gdb_exe": "C:\\Program Files\\Arm\\GNU Toolchain mingw-w64-x86_64-arm-none-eabi\\bin\\arm-none-eabi-gdb.exe",
  "serial_no": "",
  "rtt_telnet_port": 0,
  "swo_command": [],
  "operation_mode": 1
}
  • exe:JLink.exe 完整路径(必填)
  • gdbserver_exe:JLinkGDBServerCL.exe 路径,RTT 和 GDB 调试需要
  • rtt_exe:JLinkRTTClient.exe 路径
  • gdb_exe:arm-none-eabi-gdb 路径,GDB 源码级调试需要
  • serial_no:默认探针序列号,多探针场景下使用
  • rtt_telnet_port:RTT 端口,0 表示使用工具默认值
  • swo_command:可选,完整 SWO viewer 命令数组,供 jlink_swo.py 包装
  • 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)

设备参数(device/interface/speed)统一在工作区的 .embeddedskills/config.json 中管理:

json
{
  "jlink": {
    "device": "STM32F407VG",
    "interface": "SWD",
    "speed": "4000"
  }
}
  • device:芯片型号(如 STM32F407VG、GD32F470ZG)
  • interface:调试接口,SWD 或 JTAG,默认 SWD
  • speed:调试速率 kHz,默认 4000

参数解析优先级:CLI 显式参数 > .embeddedskills/config.json(工程级)> skill/config.json(环境级)> .embeddedskills/state.json > 默认值/报错

成功执行后,确认过的 device/interface/speed 会自动写回工程配置。

子命令

基础操作

子命令用途风险
info探测探针与目标连通性低
flash烧录固件(.hex / .bin / .elf)高
read-mem读取内存区域低
write-mem写入内存高
regs查看 CPU 寄存器低
reset复位目标芯片高
rtt读取 RTT 日志输出低
swo包装外部 SWO viewer 为统一事件流低

在线调试(JLink Commander)

子命令用途风险
halt暂停 CPU,返回寄存器状态低
go恢复 CPU 运行低
step单步执行(支持指定步数),返回执行的指令和寄存器低
run-to设置断点并运行,等待命中后返回状态低

GDB 源码级调试

子命令用途依赖
gdb backtrace/locals查看调用栈和局部变量arm-none-eabi-gdb
gdb break/continue/next/step/finish/untilone-shot 控制执行流arm-none-eabi-gdb
gdb frame/print/watch/disassemble/threads/crash-reportone-shot 源码级诊断arm-none-eabi-gdb

执行流程

  1. 读取 skill/config.json,确认 exe 路径有效
  2. 读取 .embeddedskills/config.json 获取工程级配置(device/interface/speed)
  3. 读取 .embeddedskills/state.json 获取历史状态
  4. 参数解析优先级:CLI 显式参数 > .embeddedskills/config.json(工程级)> skill/config.json(环境级)> .embeddedskills/state.json > 默认值/报错
  5. 若当前动作需要 device 且仍为空,直接要求用户补充,绝不猜测
  6. 多探针场景未指定 serial_no 时,列出探针让用户选择,不自动选择
  7. 按 operation_mode 决定是否需要确认后执行
  8. 使用模板生成临时 .jlink 命令文件,调用 JLink.exe 时带 -NoGui 1 -ExitOnError 1 -AutoConnect 1
  9. 解析输出和返回码,返回结构化结果
  10. 成功执行后,将确认过的 device/interface/speed 写回 .embeddedskills/config.json

脚本调用

skill 目录下有四个 Python 脚本,使用标准库实现,无额外依赖。

jlink_exec.py — 基础操作 + 轻量调试

bash
# 探测连通性
python <skill-dir>/scripts/jlink_exec.py info --device GD32F470ZG --json

# 烧录固件
python <skill-dir>/scripts/jlink_exec.py flash --file build/app.hex --device GD32F470ZG --json

# 烧录 .bin(必须提供地址)
python <skill-dir>/scripts/jlink_exec.py flash --file build/app.bin --device GD32F470ZG --address 0x08000000 --json

# 读取内存
python <skill-dir>/scripts/jlink_exec.py read-mem --address 0x08000000 --length 256 --device GD32F470ZG --json

# 写入内存
python <skill-dir>/scripts/jlink_exec.py write-mem --address 0x20000000 --value 0x12345678 --device GD32F470ZG --json

# 查看寄存器
python <skill-dir>/scripts/jlink_exec.py regs --device GD32F470ZG --json

# 复位目标
python <skill-dir>/scripts/jlink_exec.py reset --device GD32F470ZG --json

# 暂停 CPU
python <skill-dir>/scripts/jlink_exec.py halt --device GD32F470ZG --json

# 恢复运行
python <skill-dir>/scripts/jlink_exec.py go --device GD32F470ZG --json

# 单步执行(3 步)
python <skill-dir>/scripts/jlink_exec.py step --device GD32F470ZG --count 3 --json

# 运行到断点地址
python <skill-dir>/scripts/jlink_exec.py run-to --device GD32F470ZG --address 0x08001234 --timeout-ms 3000 --json

通用可选参数:--interface SWD|JTAG、--speed 4000、--serial-no <序列号>、--exe <JLink.exe路径>

jlink_rtt.py — RTT 日志读取

bash
python <skill-dir>/scripts/jlink_rtt.py --device GD32F470ZG --json

可选参数:--serial-no、--channel、--encoding、--rtt-port、--gdbserver-exe <路径>、--rtt-exe <路径>

RTT 工作原理:脚本先通过 JLinkGDBServerCL.exe 建立调试连接,再启动 JLinkRTTClient.exe 读取 RTT 数据。--json 模式输出 JSON Lines。

jlink_swo.py — SWO 事件流包装

bash
# 使用 config.json 里的 swo_command
python <skill-dir>/scripts/jlink_swo.py --json

# 或显式传入 viewer 命令
python <skill-dir>/scripts/jlink_swo.py \
  --viewer-cmd JLinkSWOViewerCL.exe -device GD32F470ZG -itf SWD -speed 4000 \
  --json

jlink_swo.py 不直接实现 SWO 协议,而是把外部 viewer 的 stdout/stderr 统一包装成 JSON Lines,便于上层 workflow 或 AI 继续消费。

jlink_gdb.py — GDB 源码级调试(需要 arm-none-eabi-gdb)

bash
# 执行自定义 GDB 命令序列
python <skill-dir>/scripts/jlink_gdb.py run \
  --gdbserver-exe <路径> --gdb-exe <arm-none-eabi-gdb路径> \
  --device GD32F470ZG --elf build/app.elf \
  --commands "break main" "continue" "backtrace" "info locals" --json

# 快捷:获取调用栈
python <skill-dir>/scripts/jlink_gdb.py backtrace \
  --gdbserver-exe <路径> --gdb-exe <路径> \
  --device GD32F470ZG --elf build/app.elf --json

# 快捷:查看局部变量
python <skill-dir>/scripts/jlink_gdb.py locals \
  --gdbserver-exe <路径> --gdb-exe <路径> \
  --device GD32F470ZG --elf build/app.elf --json

GDB 调试需要 ELF 文件才能进行源码级调试(断点到函数名、查看变量)。没有 ELF 时仍可使用地址级调试。

输出格式

所有脚本以 JSON 格式返回,基础字段为 status(ok/error)、action、summary、details,并可能附带 context、artifacts、metrics、state、next_actions、timing。流式观测命令使用 JSON Lines,并统一输出 source、channel_type、stream_type。

成功示例:

json
{
  "status": "ok",
  "action": "halt",
  "summary": "已暂停,PC=0x08049ABC",
  "details": {
    "device": "GD32F470ZG",
    "registers": { "PC": "0x08049ABC", "R0": "0x00000004", "..." : "..." }
  }
}

step 示例(包含执行的指令):

json
{
  "status": "ok",
  "action": "step",
  "summary": "单步3次,PC=0x08049AB4",
  "details": {
    "steps": [
      { "address": "0x08049AB8", "opcode": "80 1B", "instruction": "SUBS R0, R0, R6" },
      { "address": "0x08049ABA", "opcode": "A8 42", "instruction": "CMP R0, R5" },
      { "address": "0x08049ABC", "opcode": "FA D3", "instruction": "BCC #-0x0C" }
    ],
    "registers": { "PC": "0x08049AB4", "..." : "..." }
  }
}

run-to 示例(断点命中):

json
{
  "status": "ok",
  "action": "run-to",
  "summary": "断点命中 @ 0x08049AB4,PC=0x08049AB4",
  "details": {
    "bp_address": "0x08049AB4",
    "bp_hit": true,
    "registers": { "PC": "0x08049AB4", "..." : "..." }
  }
}

核心规则

  • 不自动猜测 device 芯片型号,缺失时必须询问用户
  • 多探针场景不自动选择探针,必须让用户指定序列号
  • 参数解析优先级为:CLI 显式参数 > .embeddedskills/config.json(工程级)> skill/config.json(环境级)> .embeddedskills/state.json > 默认值/报错
  • .bin 文件必须显式提供烧录地址,缺失时报错
  • 连接失败时给出排查建议(检查连线、供电、接口类型、速度),不自动尝试更激进参数
  • 烧录、写内存、复位在参数完整且用户意图明确时直接执行
  • run-to 的断点在单次 JLink 会话内完成设置和清除,不存在跨会话 handle 问题
  • 结果回显中始终包含目标芯片、接口类型和执行动作
  • 产物路径(elf/file)不进入工程配置,仍只依赖 state.json
  • last_flash/last_debug 等运行状态继续写入 state.json

调试典型工作流

快速排查(JLink Commander)

halt → regs → read-mem → step → go

适合查看当前执行位置、寄存器状态、内存值,无需 ELF 和 GDB。

断点调试(JLink Commander)

run-to(address) → regs → read-mem → go

在指定地址设置断点并等待命中,查看此时的状态。

源码级调试(GDB)

gdb run --elf app.elf --commands "break main" "continue" "backtrace" "info locals"

需要 ELF 文件和 arm-none-eabi-gdb,支持函数名断点和变量查看。

参考

遇到芯片型号问题时可查阅 references/common_devices.md。

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 Jlink AI skill do?

J-Link 下载与在线调试工具,用于探测设备、烧录固件、读写内存、查看寄存器、复位目标、读取 RTT/SWO 日志, 以及在线调试(暂停/恢复/单步/断点运行/调用栈/变量查看)。 当用户提到 J-Link、JLink、RTT、烧录固件、写内存、读内存、寄存器查看、目标复位、探针连通性检查、 在线调试、单步、断点、调用栈时自动触发,也兼容 /jlink 显式调用。 即使用户只是说"烧录一下"、"看看 RTT 输出"或"调试一下",只要上下文涉及 J-Link 探针就应触发此 skill。

Why use Jlink on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhinkgit/embeddedskills/tree/main/jlink. 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 Jlink?

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 Jlink?

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

Is the Jlink 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇