Debug

nvidia/debug

Run commands inside a remote Docker container via the file-based command relay (tools/debugger). Use when the user says "run in Docker", "run on GPU", "debug remotely", "run test in container", "check nvidia-smi", "run pytest in Docker", or needs to execute any command inside a Docker container that shares the repo filesystem. Requires the user to have started server.sh inside the container first.

301 tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

3381

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/NVIDIA/Model-Optimizer --skill debug

What it tells the agent to use

found in the instruction text

Bash runs shell commands — read the instruction before connecting

The instruction itself

2 sections, as written by the author

Remote Docker Debugger

Execute commands inside a Docker container from the host using the file-based command relay.

Read tools/debugger/CLAUDE.md for full usage details — it has the protocol and examples.

Quick Reference

# Check connection
bash tools/debugger/client.sh status

# Connect to server (user must start server.sh in Docker first)
bash tools/debugger/client.sh handshake

# Run a command
bash tools/debugger/client.sh run "<command>"

# Long-running command (default timeout is 600s)
bash tools/debugger/client.sh --timeout 1800 run "<command>"

# Cancel the currently running command
bash tools/debugger/client.sh cancel

# Reconnect after server restart
bash tools/debugger/client.sh flush
bash tools/debugger/client.sh handshake

How to use it

Copy the folder

Take nvidia/debug from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.