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/quant-recipe-searchnpx skills add NVIDIA/Model-Optimizer --skill quant-recipe-searchgit 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 | $0.00148 | $0.01915 |
| Opus 5 | $0.00074 | $0.00958 |
| Sonnet 5 | $0.00030 | $0.00383 |
| Haiku 4.5 | $0.00015 | $0.00192 |
Grade A, and why
quant-recipe-search 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 3d 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quant Recipe Search
Use this skill when quantization is an iterative recipe search, not a one-off PTQ run. The skill owns strategy: define success, choose the search space, sequence candidates, and decide the next iteration. It delegates checkpoint generation, serving, evaluation, monitoring, and metric comparison to the existing execution skills.
Treat a direct request such as "find the best quantization recipe and generate a PTQ checkpoint for this model" as enough to start. Recover local state first, then ask only for missing decisions that change the search.
Skill Boundaries
- Use
ptqto produce and validate checkpoints. - Use
deploymentto serve checkpoints and debug serving-specific flags. - Use
evaluationto create NEL configs and submit evals. - Use
launching-evalsto run, resume, debug, and analyze NEL runs. - Use
monitorfor active job tracking. - Use
accessing-mlflowfor MLflow artifact lookup. - Use
compare-resultsfor validated baseline-vs-candidate deltas and score-field comparability.
Do not duplicate those workflows here. This skill should leave the user with a clear recipe portfolio, success metric, experiment sequence, and next decision.
Problem
The task is to find the best recipe for a user-defined target, not merely to produce a quantized checkpoint. A generated PTQ checkpoint is only a candidate. It becomes a recommended recipe only after evaluation and comparison against the matching baseline.
Required inputs before planning candidates:
- Optimization goal: compute/throughput, memory/latency, or a custom metric.
- Primary quantization family: for example NVFP4, W4A16 NVFP4, FP8/W8A8, INT4/AWQ, or a custom mixed set.
- Benchmark set or baseline results: the user-defined acceptance surface.
If any of these are missing, ask for them. Do not silently default to FP8/W8A8 or call a checkpoint "best" before evaluation.
Default success rule: maximize the chosen performance objective while keeping each benchmark within 1 percentage point of the matching BF16/FP16 baseline. Near-threshold or noisy regressions require reruns before making a decision.
What ships with it
2 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.
- 3d ago First seen · 168 lines · 148 tokens per session scan A b7ea8395ff0c
quant-recipe-search is a skill published in the GitHub repository NVIDIA/Model-Optimizer (3,675 stars, last pushed yesterday), licensed Apache-2.0. It adds 148 tokens to every session and 1,915 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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