Cuopt User Rules
OpenAIOfficialCreated by NVIDIA cuOpt TeamDistributed by OpenAI
- Skill
- Guidelines
- Software development
- Apache-2.0
- Medium risk
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
Download the skill as a ZIP:
https://autogineer.com/agent-skills/openai/cuopt-user-rules/downloadUnzip 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:
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?"
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
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]"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:
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.)"
Different packages by language/interface:
Language / Interface Package Check Python cuopt(pip/conda) — also pulls inlibcuoptimport cuoptC libcuopt(pip/conda) — already present ifcuoptis installedfind libcuopt.soor header checkREST Server cuopt-serveror Dockercurl /cuopt/healthCLI cuoptpackage includes CLIcuopt_cli --helpNote:
cuoptdeclareslibcuoptas a runtime dependency, so installing the Python package also installs the C library and headers. Installinglibcuopton its own does not install the Python API.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
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)
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:
| Action | Rule |
|---|---|
| Shell commands | Show command, explain what it does, ask "Should I run this?" |
| Package installs | Never run installs yourself — give the exact command, user runs it (see below). |
| Examples/scripts | Show the code first, ask "Would you like me to run this?" |
| File writes | Explain 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
sudoor run as rootModify 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.
| Forbidden | What to do instead |
|---|---|
pip install ..., conda install ..., apt install ..., any package manager | Give 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
- ATTRIBUTION.mdAdded by AutoGineer1.0 KiB
- BENCHMARK.md2.7 KiB
- LICENSEAdded by AutoGineer10.0 KiB
- SKILL.md8.1 KiB
- agents/openai.yaml211 B
- evals/evals.json1.6 KiB
- skill-card.md2.7 KiB
- skill.oms.sig4.5 KiB
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
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cuopt-user-rules.zip