accessing-mlflow
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs…
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
$ npx -y skills add nvidia/model-optimizer --skill debug --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/debugContext preview
The summary Claude sees to decide when to auto-load this skill.
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
name: debug description: 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.
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.
# 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
Documentation | Roadmap | NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, pruning, Neural Architecture Search (NAS), distillation, speculative
Query and browse evaluation results stored in MLflow. Use when the user wants to look up runs…
Inspect Hugging Face decoder layers on meta tensors and plan or run per-rank BF16, FP8, and…
Shared ModelOpt support files. Use only when another ModelOpt skill directs you here.
Establish baseline-vs-candidate evaluation plans, delegate missing evaluations, compare…
Deterministic end-to-end driver for day-0 quantized-checkpoint releases — chains PTQ →…
Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using…