Model-Optimizer AGENTS.md

Repository instructions for NVIDIA Model-Optimizer, including where code, tests, examples, and agent skills live. Model-Optimizer is software for improving and deploying machine-learning models.

In plain words
What is it for?
Use it to follow repository conventions, read the required coding guide, run focused tests, handle pre-commit checks, and prepare signed commits or pull requests.
Why use it?
It gives coding agents the project rules and orientation they need before changing code or preparing a contribution.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/nvidia/model-optimizer/agents-md
Clone the repo
git clone --depth 1 https://github.com/NVIDIA/Model-Optimizer

Made for: Codex, OpenCode.

Per session 920 This file is loaded in full into every session.
When invoked 920 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00920 $0.00920
Opus 5 $0.00460 $0.00460
Sonnet 5 $0.00184 $0.00184
Haiku 4.5 $0.00092 $0.00092

Measured yesterday against content hash f791a2eb9d3d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Model-Optimizer AGENTS.md 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 yesterday.

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.

AGENTS.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Instructions for ModelOpt

These instructions apply to AI-assisted work in this repository.

Repository orientation

  • Start with README.md for project overview and install.
  • Use modelopt/ for source, tests/ for focused test coverage, and examples/ or docs/ for usage patterns.
  • Agent skills live under plugins/modelopt/skills/, the installable plugin's canonical skill tree. .agents/skills and .claude/skills expose those skills through relative symlinks. Shared agent config and scripts remain under .agents/. See .agents/README.md for the convention.

Coding guidelines

  • Coding guide: Code development and review require reading and following the coding standards in CONTRIBUTING.md; do not skip this step.
  • Use relative paths from the repo root in commands and file references.

Iterative development

  • Running tests: Follow the writing and running tests instructions. For fast initial iteration, choose focused tests for the changed area from tests/.
  • Running pre-commit: Follow the pre-commit hook instructions. Hooks may modify files; review and re-stage those changes before committing.
  • Signed commit: Use git commit -s -S -m "<message>" for commits so they follow the signing your work requirements.
  • Never git push without explicit approval in the current turn. Commit locally is fine; publishing to a remote is not.
  • After git commit, stop and wait for the user to say "push", "publish", "ship", or equivalent before running git push, gh pr create, or any push-option flags like -o merge_request.create.

Contributing and PR readiness

  • Before opening or marking a PR ready for review, read the submitting your code guidance.
  • Read .github/PULL_REQUEST_TEMPLATE.md and satisfy the checklist.
  • PR description: fill the template sections — what changed and why, a usage snippet if it adds an API or flag, and what you actually ran under Testing. Root cause, benchmark numbers, and design rationale belong here. Don't restate the diff file by file.
  • Only changelog-worthy changes get a CHANGELOG.rst entry: new features, backward breaking changes, deprecations, and fixes for critical or known bugs from a previous release. Skip bugs introduced and fixed within the same unreleased cycle.
  • Keep each entry to one or two sentences written for external users: what changed and what they need to do. No internal bug numbers (e.g. NVBug IDs), root-cause analysis, or implementation detail — that belongs in the PR description. File features under the matching **New Features** sub-section used by recent releases (e.g. *Quantization*, *Speculative Decoding*, *Megatron Framework (M-LM / M-Bridge)*, *Misc*) rather than relabeling existing ones.

Read the full file on GitHub · 74 lines

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.

  1. yesterday First seen · 74 lines · 920 tokens per session scan A f791a2eb9d3d

Subscribe to this mod's changes

Model-Optimizer AGENTS.md is an instructions file published in the GitHub repository NVIDIA/Model-Optimizer (3,612 stars, last pushed yesterday), licensed Apache-2.0. It adds 920 tokens to every session, about $0.0046 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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