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debug

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Actualizado5 de junio de 2026, 23:12

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.

Instalación

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

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Fuente

NVIDIA

NVIDIA/Model-Optimizer

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SKILL.md

readonly

name

debug

description

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

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Use when the user asks to find, search for, or optimize the best quantization recipe for a model, including direct requests like "find the best quantization recipe and generate a PTQ checkpoint." Guides the multi-candidate loop: choose compute-vs-memory success metrics, select ModelOpt recipe baselines, design AutoQuant/manual recipe deltas, interpret sensitivity, and decide next candidates. Do NOT use for a single known PTQ recipe run (use ptq), serving (use deployment), creating/running evals (use evaluation or launching-evals), monitoring jobs (use monitor), MLflow browsing (use accessing-mlflow), or comparing completed baseline-vs-candidate scores only (use compare-results).

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Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).

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name

debug

description

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