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/day0-releasenpx skills add NVIDIA/Model-Optimizer --skill day0-releasegit 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 | $0.00150 | $0.04256 |
| Opus 5 | $0.00075 | $0.02128 |
| Sonnet 5 | $0.00030 | $0.00851 |
| Haiku 4.5 | $0.00015 | $0.00426 |
Grade A, and why
day0-release 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 — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Day-0 Release
Drive a model from a pretrained checkpoint to a publish decision for a quantized checkpoint, in a fixed sequence with a gate after every stage. This skill is a conductor: it sequences the existing domain skills and enforces the gates — it does not re-implement quantization, serving, evaluation, or comparison.
Goal (the default day-0 criterion): a quantized checkpoint smaller than the source, with accuracy drop within the threshold (default <1%) on the standard benchmark set versus the matching baseline, plus a publish recommendation.
When to use
Use only for the full goal-driven release. For a single stage, route to the domain skill directly: quantize → ptq, serve → deployment, evaluate → evaluation, compare two existing runs → compare-results.
Inputs
Resolve these before starting (ask the user for anything missing):
- Model — HF handle or checkpoint path.
- Recipe / qformat — e.g.
nvfp4,fp8, or a recipe path. One candidate for v1. - Cluster / launcher — from
clusters.yaml(see the common skill'senvironment-setup.md). - Eval set — defaults to the evaluation skill's AA suite
(
recipes/tasks/aa/). - Threshold — max accuracy drop; default
0.01(1%).
The chain
setup ─▶ PTQ ─▶ canary ─▶ baseline-eval ─▶ quantized-eval ─▶ compare ─▶ verbosity ─▶ closeout
│ │ │ │ │ │
gate_ptq /health gate_run gate_run gate_compare gate_verbosity
+ 1 gen
The evaluation skill deploys the model it evaluates (it stands up its own
endpoint per run), so there is no separate deploy stage — a serving failure
during evaluation surfaces through the eval gate (DEPLOYMENT_HEALTH_FAILED) and
triages to the deployment skill (see Step 4). The Step 2b canary is not
that: it runs before any evaluation precisely so an unservable checkpoint is
caught in ~15 min rather than after a multi-hour eval.
What ships with it
6 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 · 313 lines · 150 tokens per session scan A 596a7a649ee9
day0-release is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,675 stars, last pushed today), licensed Apache-2.0. It adds 150 tokens to every session and 4,256 once invoked, about $0.0007 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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