NVIDIA cuOpt is an optimization engine that uses GPUs to solve mathematical planning problems such as linear programming, quadratic programming, and vehicle routing. Developers and operations teams use its C++, C, Python, and server APIs for large-scale optimization, while the catalogue's agent skills connect it with agent-based optimization workflows.
Borrowing it
Nothing to install: this file belongs to NVIDIA/cuopt. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NVIDIA/cuopt/main/AGENTS.mdgit clone --depth 1 https://github.com/NVIDIA/cuoptWrote 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/instructions/nvidia/cuopt/agents-md)<a href="https://agentmods.dev/instructions/nvidia/cuopt/agents-md"><img src="https://agentmods.dev/badge/instructions/nvidia/cuopt/agents-md/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/instructions/nvidia/cuopt/agents-md"><img src="https://agentmods.dev/badge/instructions/nvidia/cuopt/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00913 | $0.00913 |
| Opus 5.5 | $0.00365 | $0.00365 |
| Sonnet 5.5 | $0.00183 | $0.00183 |
| Haiku 4.5 | $0.00091 | $0.00091 |
Grade A, and why
cuopt 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 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — cuOpt AI Agent Entry Point
AI agent skills for NVIDIA cuOpt optimization engine. Skills live in skills/ (repo root) and use a flat layout: per domain, a concept skill (formulation / problem types) plus implementation skills — typically one per interface (Python, C, CLI, server), or consolidated when the content is shared across interfaces (e.g. installation). Skills evolve through agent interactions — see skills/cuopt-skill-evolution/ for the evolution workflow.
🔒 MANDATORY — Ambiguity: When the problem could be read more than one way, you MUST either ask the user to clarify or solve every plausible interpretation and report all outcomes. Never pick one interpretation silently.
Skills directory (flat)
Rules
skills/cuopt-user-rules/— Base rules for end users calling cuOpt (routing, LP, MILP, QP, install, server). Not for cuOpt internals — seeskills/cuopt-developer/. Read first for user-facing tasks; choose skills from the index below by task and interface.skills/cuopt-developer/— Modify, build, test, debug, and contribute to cuOpt internals (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions. For VRP dimension work (combine invariants, fwd/bwd propagation, new constraints/objectives in the routing engine), readskills/cuopt-developer/references/vrp_skills.mdin addition toSKILL.md.skills/cuopt-skill-evolution/— Skill evolution: after solving a non-trivial problem, propose skill updates to capture generalizable learnings.
Common (concepts only; no API code)
skills/cuopt-numerical-optimization-formulation/— LP / MILP / QP: concepts + problem parsing + common formulation patternsskills/cuopt-multi-objective-exploration/— Multi-objective: trace + interpret the Pareto frontier across competing objectives (ε-constraint / weighted-sum over repeated cuOpt solves)
Installation
skills/cuopt-install/— User install for Python, C, and server (pip, conda, Docker, verification). For building cuOpt from source, seeskills/cuopt-developer/.
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 · 54 lines · 913 tokens per session scan A beb5c6e963c3
cuopt AGENTS.md is an instructions file published in the GitHub repository NVIDIA/cuopt (1,059 stars, last pushed today), licensed Apache-2.0. It adds 913 tokens to every session, about $0.0037 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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