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 agentmods add skills/nvidia/model-optimizer/ptqnpx skills add NVIDIA/Model-Optimizer --skill ptqgit clone --depth 1 https://github.com/NVIDIA/Model-OptimizerWhat 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 | $0.00106 | $0.02893 |
| Opus 5 | $0.00053 | $0.01447 |
| Sonnet 5 | $0.00021 | $0.00579 |
| Haiku 4.5 | $0.00011 | $0.00289 |
Grade A, and why
ptq 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 2d 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 — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ModelOpt Post-Training Quantization
Produce a quantized checkpoint from a pretrained model. Read examples/hf_ptq/README.md first — it has the support matrix, CLI flags, and accuracy guidance.
Use quant-recipe-search for multi-candidate recipe exploration or
optimization. Use this skill for each selected recipe's PTQ run.
Step 1 — Environment
Use the common skill's environment-setup.md and workspace-management.md.
After completing them you should know:
- ModelOpt source is available
- Local or remote (+ cluster config if remote)
- SLURM / Docker+GPU / bare GPU
- Launcher available?
- Which workspace to use
Step 2 — Is the model supported?
Check the support table in examples/hf_ptq/README.md for verified HF models.
- Listed → supported, use
hf_ptq.py(step 4A/4B) - Not listed → read
references/unsupported-models.mdto determine ifhf_ptq.pycan still work or if a custom script is needed (step 4C)
Step 2.5 — Check for model-specific dependencies
If the model uses trust_remote_code (check config.json for auto_map), inspect its custom Python files for imports not present in the container:
grep -h "^from \|^import " <model_path>/modeling_*.py | sort -u
Known dependency patterns:
| Import found | Packages to install |
|---|---|
from mamba_ssm / from causal_conv1d |
mamba-ssm causal-conv1d (Mamba/hybrid models: NemotronH, Jamba) |
If extra deps are needed:
- Launcher (4B): set
EXTRA_PIP_DEPSin the task'senvironmentsection —ptq.shinstalls them automatically - Manual (4A):
unset PIP_CONSTRAINT && pip install <deps>before runninghf_ptq.py
Step 3 — Choose quantization format
First, check for a model-specific recipe:
ls modelopt_recipes/models/ 2>/dev/null
ls modelopt_recipes/huggingface/<model_type>/ptq/ 2>/dev/null # per-arch; <model_type> from local config.json (Hub ID: AutoConfig.from_pretrained)
If a model-specific recipe exists, prefer --recipe <path> — but inspect its include/exclude patterns rather than assuming (e.g. for VLMs, confirm the vision tower is actually excluded).
What ships with it
5 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.
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.
- 2d ago First seen · 215 lines · 106 tokens per session scan A 2cddd9d95def
ptq is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,612 stars, last pushed 2d ago), licensed Apache-2.0. It adds 106 tokens to every session and 2,893 once invoked, about $0.0005 per session on Opus 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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