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cuopt-user-rules

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by openai · part of openai/plugins

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

🧩 One of 7 skills in the openai/plugins package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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.


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?"

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

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

Resources

Documentation

Examples

Support