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Not for cuOpt internals — use cuopt-developer for those.\nlicense: Apache-2.0\nmetadata:\n  author: NVIDIA cuOpt Team\n  tags:\n    - cuopt\n    - user-rules\n    - guidelines\n---\n\n\n\n\n# cuOpt User Rules\n\n**Read this when helping someone *use* cuOpt** (calling the SDK, installing, deploying the server). For modifying cuOpt itself, switch to `cuopt-developer`.\n\n---\n\n## Ask Before Assuming\n\n**Always clarify ambiguous requirements before implementing:**\n\n- What **language\u002Finterface**?\n- What problem type?\n- What constraints matter?\n- What output format?\n\n**Skip asking only if:**\n- User explicitly stated the requirement\n- Context makes it unambiguous (e.g., user shows Python code)\n\n---\n\n## Handle Incomplete Questions\n\n**If a question seems partial or incomplete, ask follow-up questions:**\n\n- \"Could you tell me more about [missing detail]?\"\n- \"What specifically would you like to achieve with this?\"\n- \"Are there any constraints or requirements I should know about?\"\n\n**Common missing information to probe for:**\n- Problem size (number of vehicles, locations, variables, constraints)\n- Specific constraints (time windows, capacities, precedence)\n- Performance requirements (time limits, solution quality)\n- Integration context (existing codebase, deployment environment)\n\n**Don't guess — ask.** A brief clarifying question saves time vs. solving the wrong problem.\n\n---\n\n## Clarify Data Requirements\n\n**Before generating examples, ask about data:**\n\n1. **Check if user has data:**\n   - \"Do you have specific data you'd like to use, or should I create a sample dataset?\"\n   - \"Can you share the format of your input data?\"\n\n2. **If using synthesized data:**\n   - State clearly: \"I'll create a sample dataset for demonstration\"\n   - Keep it small and understandable (e.g., 5-10 locations, 2-3 vehicles)\n   - Make values realistic and meaningful\n\n3. **Always document what you used:**\n   ```\n   \"For this example I'm using:\n   - [X] locations\u002Fvariables\u002Fconstraints\n   - [Key assumptions: e.g., all vehicles start at depot, 8-hour shifts]\n   - [Data source: synthesized \u002F user-provided \u002F from docs]\"\n   ```\n\n4. **State assumptions explicitly:**\n   - \"I'm assuming [X] — let me know if this differs from your scenario\"\n   - List any default values or simplifications made\n\n---\n\n## MUST Verify Understanding\n\n**Before writing substantial code, you MUST confirm your understanding:**\n\n```\n\"Let me confirm I understand:\n- Problem: [restate in your words]\n- Constraints: [list them]\n- Objective: [minimize\u002Fmaximize what]\n- Interface: [Python\u002FREST\u002FC\u002FCLI]\nIs this correct?\"\n```\n\n---\n\n## Follow Requirements Exactly\n\n- Use the **exact** variable names, formats, and structures the user specifies\n- Don't add features the user didn't ask for\n- Don't change the problem formulation unless asked\n- If user provides partial code, extend it—don't rewrite from scratch\n\n---\n\n## Check Results\n\nAfter providing a solution, guide the user to verify:\n\n- **Status check**: Is it `Optimal` \u002F `FeasibleFound` \u002F `SUCCESS`?\n- **Constraint satisfaction**: Are all constraints met?\n- **Objective value**: Is it reasonable for the problem?\n\n**Always end with a Result summary** that includes at least:\n- Solver status (e.g. Optimal, FeasibleFound, SUCCESS).\n- **Objective value with highlight** — easy to spot (bold or code block). Example: **Objective value (min total cost):** <value> or `Objective value: \u003Cvalue>`.\n- Briefly what the objective represents (e.g. total cost, total profit).\n\nDo 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).\n\n**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.\n\nProvide diagnostic code snippets when helpful.\n\n### Post-correction check (mandatory)\nIf the result required a correction, retry, or workaround to reach this point, you MUST evaluate the skill-evolution workflow (`skills\u002Fskill-evolution\u002FSKILL.md`) before moving on. Do not skip this step.\n\n---\n\n## Check Environment First\n\n**Before writing code or suggesting installation, verify the user's setup:**\n\n1. **Ask how they access cuOpt:**\n   - \"Do you have cuOpt installed? If so, which interface?\"\n   - \"What environment are you using? (local GPU, cloud, Docker, server, etc.)\"\n\n2. **Different packages by language\u002Finterface:**\n\n   | Language \u002F Interface | Package | Check |\n   |----------------------|---------|-------|\n   | **Python** | `cuopt` (pip\u002Fconda) — also pulls in `libcuopt` | `import cuopt` |\n   | **C** | `libcuopt` (pip\u002Fconda) — already present if `cuopt` is installed | `find libcuopt.so` or header check |\n   | REST Server | `cuopt-server` or Docker | `curl \u002Fcuopt\u002Fhealth` |\n   | CLI | `cuopt` package includes CLI | `cuopt_cli --help` |\n\n   **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.\n\n3. **If not installed, ask how they want to access:**\n   - \"Would you like help installing cuOpt, or do you have access another way?\"\n   - Options: pip, conda, Docker, cloud instance, existing remote server\n\n4. **Never assume installation is needed** — the user may:\n   - Already have it installed\n   - Be connecting to a remote server\n   - Prefer a specific installation method\n   - Only need the C library (not Python)\n\n5. **Ask before running any verification commands:**\n   ```python\n   # Python API check - ask first\n   import cuopt\n   print(cuopt.__version__)\n   ```\n   ```bash\n   # C API check - ask first\n   find ${CONDA_PREFIX} -name \"libcuopt.so\"\n   ```\n   ```bash\n   # Server check - ask first\n   curl http:\u002F\u002Flocalhost:8000\u002Fcuopt\u002Fhealth\n   ```\n\n---\n\n## Ask Before Running\n\n**Do not execute commands or code without explicit permission:**\n\n| Action | Rule |\n|--------|------|\n| Shell commands | Show command, explain what it does, ask \"Should I run this?\" |\n| Package installs | Allowed in user space (`pip`\u002F`conda`\u002FDocker) once the user confirms they want cuOpt installed — see below. Only `sudo`\u002Fsystem-level installs are off-limits. |\n| Examples\u002Fscripts | Show the code first, ask \"Would you like me to run this?\" |\n| File writes | Explain what will change, ask before writing |\n\n**Exceptions (okay without asking):**\n- Read-only commands the user explicitly requested\n- Commands the user just provided and asked you to run\n\n---\n\n## No Privileged Operations\n\n> **🔒 MANDATORY — this is the one non-negotiable refusal.** It applies even when the user explicitly asks.\n\n**Never do these:**\n\n- Use `sudo` or run as root\n- Modify system files or configurations (e.g. `\u002Fetc`)\n- Add system-level package repositories or keys\n- Change firewall, network, or driver settings\n\nIf a task seems to need one of these, stop and explain what's needed — the user runs the privileged step themselves. Installs into a user-space environment (a virtualenv, a conda env, or the active Python) are **not** privileged and are covered below.\n\n---\n\n## Installing Packages\n\nInstalling cuOpt (and the packages it needs) **in user space** is allowed — that's what the `cuopt-install` skill is for. The rule is *get the user's go-ahead*, not *refuse*:\n\n1. **Confirm first.** Tell the user which package you'll install, which command, and why; install once they agree. (Check the environment first per [Check Environment First](#check-environment-first) — they may already have it, or prefer a different method.)\n2. **Stay in user space.** Use `pip`, `conda`\u002F`mamba`, or Docker into the active env. 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