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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA/Model-Optimizer --skill monitorgit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWrote 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.
[](https://agentmods.dev/skills/nvidia/model-optimizer/monitor)<a href="https://agentmods.dev/skills/nvidia/model-optimizer/monitor"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/monitor/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.
<a href="https://agentmods.dev/skills/nvidia/model-optimizer/monitor"><img src="https://agentmods.dev/badge/skills/nvidia/model-optimizer/monitor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.01809 |
| Opus 5.5 | $0.00037 | $0.00724 |
| Sonnet 5.5 | $0.00019 | $0.00362 |
| Haiku 4.5 | $0.00009 | $0.00181 |
Grade A, and why
monitor 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.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Job Monitor
Monitor jobs submitted to SLURM clusters — PTQ quantization, NEL evaluation, model deployment, or raw SLURM jobs.
When to use
- Auto-monitor — another skill (PTQ, evaluation, deployment) just submitted a job. Register the job and set up monitoring immediately.
- User-initiated — user asks about a job status. Check the current session registry first; if the job is not registered there, use the discovery steps below.
Job Registry
Active jobs are tracked in per-session registries under .claude/agents/.
This avoids multiple agents clobbering one shared registry when they run at
the same time.
Use the current agent session id as <session_id>:
- Claude Code:
$CLAUDE_CODE_SESSION_ID, or thesession_idfield from hook input - Codex:
$CODEX_THREAD_ID - If no session id is available, create a stable id for the current terminal session and reuse it for every job registered by that agent
Registry layout:
.claude/agents/
<session_id>/
active_jobs.json
Each session's active_jobs.json is a JSON array:
[
{
"type": "nel",
"id": "<invocation_id or slurm_job_id>",
"host": "<cluster_hostname>",
"user": "<ssh_user>",
"submitted": "YYYY-MM-DD HH:MM",
"description": "<what this job does>",
"last_status": "<last known status>",
"owner": {
"agent": "claude-code|codex|manual",
"session_id": "<session_id>"
}
}
]
type is one of: nel, slurm, launcher.
On Job Submission
Every time a job is submitted (by any skill or manually):
- Add an entry to
.claude/agents/<session_id>/active_jobs.json. Create the session directory and file if they don't exist. - Start a durable monitor (if one isn't already watching the registry) that polls this session's registered jobs until they reach terminal status. Prefer the Claude Code
Monitortool when it is available: write a small watcher that reads.claude/agents/<session_id>/active_jobs.json, checks every job with the appropriate method below, prints state-change events, updateslast_status, removes terminal jobs from the session registry, and exits when no active jobs remain for this session.
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 · 172 lines · 93 tokens per session scan A d3f15c54d135
monitor is a skill published in the GitHub repository NVIDIA/Model-Optimizer (5,398 stars, last pushed today), licensed Apache-2.0. It adds 93 tokens to every session and 1,809 once invoked, about $0.0004 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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