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Cuopt User Rules

OpenAIOfficialCreated by NVIDIA cuOpt TeamDistributed by OpenAI

  • Skill
  • Guidelines
  • Software development
  • Apache-2.0
  • Medium risk

From the author

Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.
— the description by NVIDIA cuOpt Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured framework for AI agents to assist users with NVIDIA cuOpt. It emphasizes clarifying user requirements, verifying environment configurations, and ensuring safe interaction practices. The guidelines mandate that agents must not execute package installations or system-level changes directly, instead providing the user with the exact commands to run. It also outlines requirements for confirming problem understanding before generating code and standardizing the reporting of solver results.

What it does

  • Defines protocols for clarifying user requirements and problem constraints.
  • Provides instructions for verifying user environments and cuOpt access methods.
  • Mandates explicit confirmation of problem understanding before generating code.
  • Establishes a standard format for reporting solver status and objective values.
  • Enforces safety rules regarding package installation and system modifications.
  • Directs users to official documentation and support resources.

When to use it

  • When assisting a user with installing or configuring the cuOpt SDK or server.
  • When helping a user formulate or solve routing, LP, MILP, or QP problems using cuOpt.
  • When troubleshooting cuOpt integration issues in a user's codebase.
  • When preparing to generate code examples for cuOpt users.

Advantages

  • Ensures consistent and safe interactions by preventing unauthorized system changes.
  • Reduces errors by requiring verification of problem constraints and data formats.
  • Standardizes the output format for solver results to improve clarity.
  • Promotes clear communication by mandating follow-up questions for ambiguous requests.

Usage examples

“How do I install cuOpt for Python?”
The agent will ask about the user's current environment, explain the dependency on the Python package, and provide the specific pip or conda command for the user to execute.
“Can you help me solve this vehicle routing problem?”
The agent will first ask for details on the number of vehicles, locations, and specific constraints, then restate the problem to confirm understanding before providing any code.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/openai/cuopt-user-rules/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip cuopt-user-rules.zip -d .agents/skills

Documentation by NVIDIA cuOpt Team

cuOpt User Rules

Read this when helping someone use cuOpt (calling the SDK, installing, deploying the server). For modifying cuOpt itself, switch to cuopt-developer.


Ask Before Assuming

Always clarify ambiguous requirements before implementing:

  • What language/interface?

  • What problem type?

  • What constraints matter?

  • What output format?

Skip asking only if:

  • User explicitly stated the requirement

  • Context makes it unambiguous (e.g., user shows Python code)


Handle Incomplete Questions

If a question seems partial or incomplete, ask follow-up questions:

  • "Could you tell me more about [missing detail]?"

  • "What specifically would you like to achieve with this?"

  • "Are there any constraints or requirements I should know about?"

Common missing information to probe for:

  • Problem size (number of vehicles, locations, variables, constraints)

  • Specific constraints (time windows, capacities, precedence)

  • Performance requirements (time limits, solution quality)

  • Integration context (existing codebase, deployment environment)

Don't guess — ask. A brief clarifying question saves time vs. solving the wrong problem.


Clarify Data Requirements

Before generating examples, ask about data:

  1. Check if user has data:

    • "Do you have specific data you'd like to use, or should I create a sample dataset?"

    • "Can you share the format of your input data?"

  2. If using synthesized data:

    • State clearly: "I'll create a sample dataset for demonstration"

    • Keep it small and understandable (e.g., 5-10 locations, 2-3 vehicles)

    • Make values realistic and meaningful

  3. Always document what you used:

    "For this example I'm using:
    - [X] locations/variables/constraints
    - [Key assumptions: e.g., all vehicles start at depot, 8-hour shifts]
    - [Data source: synthesized / user-provided / from docs]"
  4. State assumptions explicitly:

    • "I'm assuming [X] — let me know if this differs from your scenario"

    • List any default values or simplifications made


MUST Verify Understanding

Before writing substantial code, you MUST confirm your understanding:

"Let me confirm I understand:
- Problem: [restate in your words]
- Constraints: [list them]
- Objective: [minimize/maximize what]
- Interface: [Python/REST/C/CLI]
Is this correct?"

Follow Requirements Exactly

  • Use the exact variable names, formats, and structures the user specifies

  • Don't add features the user didn't ask for

  • Don't change the problem formulation unless asked

  • If user provides partial code, extend it—don't rewrite from scratch


Check Results

After providing a solution, guide the user to verify:

  • Status check: Is it Optimal / FeasibleFound / SUCCESS?

  • Constraint satisfaction: Are all constraints met?

  • Objective value: Is it reasonable for the problem?

Always end with a Result summary that includes at least:

  • Solver status (e.g. Optimal, FeasibleFound, SUCCESS).

  • Objective value with highlight — easy to spot (bold or code block). Example: Objective value (min total cost): <value> or Objective value: <value>.

  • Briefly what the objective represents (e.g. total cost, total profit).

Do not bury the objective value only in the middle of a paragraph; it must appear prominently in this summary. Use sufficient precision (don't truncate or round unnecessarily unless the problem asks for it).

Workflow: Formulate once carefully (with verified understanding), solve, then sanity-check the result. If something is wrong, fix it with a targeted change—avoid spinning through many model variants. Decide, implement, verify, then move on.

Provide diagnostic code snippets when helpful.

Post-correction check (mandatory)

If the result required a correction, retry, or workaround to reach this point, you MUST evaluate the skill-evolution workflow (skills/skill-evolution/SKILL.md) before moving on. Do not skip this step.


Check Environment First

Before writing code or suggesting installation, verify the user's setup:

  1. Ask how they access cuOpt:

    • "Do you have cuOpt installed? If so, which interface?"

    • "What environment are you using? (local GPU, cloud, Docker, server, etc.)"

  2. Different packages by language/interface:

    Language / InterfacePackageCheck
    Pythoncuopt (pip/conda) — also pulls in libcuoptimport cuopt
    Clibcuopt (pip/conda) — already present if cuopt is installedfind libcuopt.so or header check
    REST Servercuopt-server or Dockercurl /cuopt/health
    CLIcuopt package includes CLIcuopt_cli --help

    Note: cuopt declares libcuopt as a runtime dependency, so installing the Python package also installs the C library and headers. Installing libcuopt on its own does not install the Python API.

  3. If not installed, ask how they want to access:

    • "Would you like help installing cuOpt, or do you have access another way?"

    • Options: pip, conda, Docker, cloud instance, existing remote server

  4. Never assume installation is needed — the user may:

    • Already have it installed

    • Be connecting to a remote server

    • Prefer a specific installation method

    • Only need the C library (not Python)

  5. Ask before running any verification commands:

    # Python API check - ask first
    import cuopt
    print(cuopt.__version__)
    # C API check - ask first
    find ${CONDA_PREFIX} -name "libcuopt.so"
    # Server check - ask first
    curl http://localhost:8000/cuopt/health

Ask Before Running

Do not execute commands or code without explicit permission:

ActionRule
Shell commandsShow command, explain what it does, ask "Should I run this?"
Package installsNever run installs yourself — give the exact command, user runs it (see below).
Examples/scriptsShow the code first, ask "Would you like me to run this?"
File writesExplain what will change, ask before writing

Exceptions (okay without asking):

  • Read-only commands the user explicitly requested

  • Commands the user just provided and asked you to run


No Privileged Operations

Never do these without explicit user request AND confirmation:

  • Use sudo or run as root

  • Modify system files or configurations

  • Add package repositories or keys

  • Change firewall, network, or driver settings

  • Write files outside the workspace


Never Install Packages Automatically

🔒 MANDATORY — You MUST NOT install, upgrade, or modify packages. Provide the exact command; the user runs it. No exceptions.

ForbiddenWhat to do instead
pip install ..., conda install ..., apt install ..., any package managerGive the exact command and ask the user to run it. Say why the package is needed.

When a package is needed: Identify it, provide the exact command, explain why, then wait for the user to confirm they ran it. Even if the user says "just install it", give the command and require them to execute it themselves.


Resources

Documentation
Examples
Support

Files

LICENSE

Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/

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Requirements and compatibility

Written for: OpenAI Codex

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs network access, Needs Python

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA cuOpt Team
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/nvidia/skills/cuopt-user-rules · commit d420d13
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (written by AutoGineer)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA cuOpt Team or OpenAI.

Download ZIP

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