What is launching-evals?

A guide for running and investigating evaluations with nemo-evaluator-launcher. An evaluation is a controlled test of an AI system, often run across selected tasks and samples to measure its behavior.

Use it to start evaluations from configuration files, run selected tasks, limit samples, inspect status and job information, and collect logs or result artifacts from local or remote runs.

It provides commands for previewing a run, checking progress, finding output paths, copying logs, and diagnosing failures. This avoids having to remember the launcher’s commands and where results are stored.

launching-evals — Skill for Claude Code by NVIDIA/Model-Optimizer

About the project

NVIDIA Model Optimizer is a library that changes deep-learning models through techniques such as quantization, pruning, distillation, neural architecture search, speculative decoding, and sparsity so they can run more efficiently. Developers use it to optimize Hugging Face, PyTorch, or ONNX models and export checkpoints for inference frameworks such as SGLang, TensorRT-LLM, TensorRT, or vLLM. The catalogue entries provide skills, instructions, and a plugin for using its workflows.

NVIDIA/Model-Optimizer · 5,398 stars · on GitHub · nvidia.github.io

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/NVIDIA/Model-Optimizer

agentmods

npx agentmods add skills/nvidia/model-optimizer/launching-evals

Made for: Claude Code.

Or install modelopt, the plugin that ships this one along with the rest of its 16 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nvidia/model-optimizer/launching-evals/github.svg)](https://agentmods.dev/skills/nvidia/model-optimizer/launching-evals)

Your own site

<a href="https://agentmods.dev/skills/nvidia/model-optimizer/launching-evals"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/launching-evals/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nvidia/model-optimizer/launching-evals"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/launching-evals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 115 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,480 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00115	$0.01480
Opus 5.5	$0.00046	$0.00592
Sonnet 5.5	$0.00023	$0.00296
Haiku 4.5	$0.00012	$0.00148

Measured 1mo ago against content hash c2a3e3a17935, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

launching-evals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/modelopt/skills/launching-evals/SKILL.md · 72 lines

How it starts

The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NeMo Evaluator Skill

Quick Reference

nemo-evaluator-launcher CLI

# Run evaluation
uv run nemo-evaluator-launcher run --config <path.yaml>
uv run nemo-evaluator-launcher run --config <path.yaml> -t <a_single_task_to_be_run_by_name>
uv run nemo-evaluator-launcher run --config <path.yaml> -t <task_name_1> -t <task_name_2> ...
uv run nemo-evaluator-launcher run --config <path.yaml> -o evaluation.nemo_evaluator_config.config.params.limit_samples=10 ...

# Preview the resolved config and the sbatch script without running the evaluation
uv run nemo-evaluator-launcher run --config <path.yaml> --dry-run

# Check status (--json for machine-readable output)
uv run nemo-evaluator-launcher status <invocation_id> --json

# Get evaluation run info (output paths, slurm job IDs, cluster hostname, etc.)
uv run nemo-evaluator-launcher info <invocation_id>

# Copy just the logs (quick — good for debugging)
uv run nemo-evaluator-launcher info <invocation_id> --copy-logs ./evaluation-results/

# For artifacts: use `nel info` to discover paths. If remote, SSH to explore and rsync what you need.
# If local, just read directly from the paths shown by `nel info`.
# ssh <user>@<hostname> "ls <artifacts_path>/"
# rsync -avzP <user>@<hostname>:<artifacts_path>/{results.yml,eval_factory_metrics.json,config.yml} ./evaluation-results/<invocation_id>.<job_index>/artifacts/

# Resume a failed/interrupted run (re-sbatches existing run.sub in the original run directory)
uv run nemo-evaluator-launcher resume <invocation_id>

# List past runs
uv run nemo-evaluator-launcher ls runs --since 1d   

# List available evaluation tasks (by default, only shows tasks from the latest released containers)
uv run nemo-evaluator-launcher ls tasks
uv run nemo-evaluator-launcher ls tasks --from_container nvcr.io/nvidia/eval-factory/simple-evals:26.03

Workflow

The complete evaluation workflow is divided into the following steps you should follow IN ORDER.

Create or modify a config using the nel-assistant skill. If the user provides a past run, use its config.yml artifact as a starting point.
Run the evaluation. See references/run-evaluation.md when executing this step.
Monitor progress (MANDATORY after every nel run): poll status repeatedly until SUCCESS/FAILED. See references/check-progress.md.
Post-run actions (when terminal state reached):
1. When the evaluation status is SUCCESS, analyze the results. See references/analyze-results.md when executing this step.
2. When the evaluation status is FAILED, debug the failed run. See references/debug-failed-runs.md when executing this step.

Read the full file on GitHub · 72 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

1mo ago First seen · 72 lines · 115 tokens per session scan A c2a3e3a17935

Subscribe to this mod's changes

launching-evals is a skill published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 115 tokens to every session and 1,480 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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