Rspack Debugging

Organization

rstackjs

rspack-debugging

Debug native Rspack crashes, segmentation faults, deadlocks, or stuck builds with LLDB and matching debug symbols.

Overview

Publisher	rstackjs
Repository	`agent-skills`
Skill name	`rspack-debugging`
Stars	93
Forks	4
Bundled files	8
License	MIT
Links	Skill source Repository

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.
8 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by rstackjs on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/rstackjs/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/rspack-debugging .claude/skills/rspack-debugging

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rspack Debugging 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 Rspack Debugging 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 Rspack Debugging 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.

Rspack debugging

Overview

This Skill guides you on how to capture the underlying crash state of Rspack (which is based on Rust). By using the LLDB debugger and Rspack packages with debug symbols, we can obtain detailed stack backtraces, which are crucial for pinpointing issues. The guides focus on non-interactive, automated debugging to easily capture backtraces.

Preparation

Before starting, please ensure your environment meets the requirements.

Install LLDB: You must install the LLDB debugger.
- macOS: Run xcode-select --install
- Linux: Install the lldb package (e.g., apt-get install lldb)
- Detailed guide: references/lldb.md
Replace Debug Packages: Production packages like @rspack/core have debug symbols stripped. They must be replaced with the @rspack-debug/* series packages to see useful stack information.

Automatic Replacement Script:

Resolve the bundled scripts/setup_debug_deps.cjs relative to the Skill root while keeping the working directory in the user's project, then run:
```
bash
node "<skill-root>/scripts/setup_debug_deps.cjs"
```
Running the above script will automatically add pnpm.overrides configuration to package.json, pointing Rspack packages to their corresponding Debug versions. Afterwards, please be sure to run pnpm install to update dependencies.

Debugging workflows

Identify your specific scenario and follow the corresponding linked guide.

Detailed guides

Guide A: crash during HMR

Scenario: Stable Crash/Deadlock during DevServer HMR. Read Guide: references/guide_a_hmr_crash.md

Guide B: crash during build

Scenario: Stable Crash/Deadlock during Build (or Unstable Build Crash that is frequent enough). Read Guide: references/guide_b_build_crash.md

Guide C: attach to stuck process

Scenario: Unstable Deadlock during Build (happens randomly). Read Guide: references/guide_c_attach_to_stuck_process.md

Guide D: coredump analysis (Dev)

Scenario: Unstable Crash during DevServer HMR (hard to catch interactively). Read Guide: references/guide_d_coredump_analysis_dev.md

Guide E: coredump analysis (Build)

Scenario: Unstable Crash during Build. Read Guide: references/guide_e_coredump_analysis_build.md

Guide F: async deadlock identification

Scenario: Unstable Async Deadlock. Main thread stuck in uv_run. Read Guide: references/guide_f_async_deadlock.md

Saving debug artifacts

Critical Instruction for Agents: When you successfully obtain a backtrace or a tracing log, you MUST save it to a local file in the user's project directory so it is preserved after the session.

Create Directory: Ensure a directory named debug_artifacts exists in the project root.
Save Backtraces: Write the full output of thread backtrace all to debug_artifacts/backtrace_<timestamp>.txt.
Save Tracing Logs: (Only if using Tracing Skill)

Environment restoration

After debugging is complete, restore your package.json to use production packages:

bash
node "<skill-root>/scripts/setup_debug_deps.cjs" --restore
pnpm install

Use the same resolved <skill-root> as in Preparation.

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 Rspack Debugging AI skill do?

Debug native Rspack crashes, segmentation faults, deadlocks, or stuck builds with LLDB and matching debug symbols.

Why use Rspack Debugging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rstackjs/agent-skills/tree/main/skills/rspack-debugging. 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 Rspack Debugging?

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 Rspack Debugging?

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

Is the Rspack Debugging AI skill free?

Yes. It is published on GitHub by rstackjs 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Rspack debugging

Overview

Preparation

Debugging workflows

Detailed guides

Detailed guides

Guide A: crash during HMR

Guide B: crash during build

Guide C: attach to stuck process

Guide D: coredump analysis (Dev)

Guide E: coredump analysis (Build)

Guide F: async deadlock identification

Saving debug artifacts

Environment restoration

Bundled files

Frequently asked questions

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