X Openai Cuopt User Rules 579c3de

---
name: x-openai-cuopt-user-rules-579c3de
description: Base rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.
license: Apache-2.0
metadata:
  author: NVIDIA cuOpt Team
  tags: '[''cuopt'', ''user-rules'', ''guidelines'']'
---

### 导出副本边界与技能别名

迁移、安装、修复和经验回写只作用于当前加载的目标宿主技能副本。原 Codex 技能/MCP、下载的源码和 `original-skills` 均为来源材料，不因执行本副本而写入；只有用户另行明确要求配置 Codex 产品本身时，才按该产品流程处理指定目标。脚本需要安装目录或状态目录时，显式提供目标路径；不得从 `CODEX_HOME` 隐式推断写入位置。

跨技能名称可能沿用源库别名。先查 [技能别名表](../../skill-aliases.json)，优先选择同来源、同插件的条目，再使用其 `id` 调用或读取对应入口；不得假定旧 `$skill`、`superpowers:skill` 名称在新宿主直接注册。不能确定目标时先核对，不替换真实产品命令或 Shell 环境变量。

## 跨宿主使用约定

本副本供支持 Agent Skills 的客户端使用。先检查当前宿主实际提供的工具、权限和已连接服务，再执行正文工作流。原文中的宿主工具标识是能力参考；只有实际发现该工具时才可调用，其他宿主应使用经核验的等价 MCP、CLI 或 API。没有等价能力的步骤必须报告不可用，不能虚构调用或完成状态。

技能资源以本技能目录为基准；输出写入当前项目中用户指定的位置。通过技能名称或自然语言触发，具体快捷命令由宿主决定。涉及真实产品、模型 API、协议字段和官方网址的名称保持原意。授权、审批、隐私和工具范围限制在迁移后仍然有效；不得为兼容而扩大权限。具体依赖与迁移说明见同目录 `PORTABILITY.md`。

# 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):** &lt;value&gt; 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 / Interface | Package | Check |
   |----------------------|---------|-------|
   | **Python** | `cuopt` (pip/conda) — also pulls in `libcuopt` | `import cuopt` |
   | **C** | `libcuopt` (pip/conda) — already present if `cuopt` is installed | `find libcuopt.so` or header check |
   | REST Server | `cuopt-server` or Docker | `curl /cuopt/health` |
   | CLI | `cuopt` package includes CLI | `cuopt_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
   # Python API check - ask first
   import cuopt
   print(cuopt.__version__)
   ```
   ```bash
   # C API check - ask first
   find ${CONDA_PREFIX} -name "libcuopt.so"
   ```
   ```bash
   # 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 `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.

| 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
- [cuOpt User Guide](https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html)
- [API Reference](https://docs.nvidia.com/cuopt/user-guide/latest/api.html)

### Examples
- [cuopt-examples repo](https://github.com/NVIDIA/cuopt-examples)
- [Google Colab notebooks](https://colab.research.google.com/github/nvidia/cuopt-examples/)

### Support
- [File a Bug](https://github.com/NVIDIA/cuopt/issues/new?template=bug_report.md)
- [Ask a Question](https://github.com/NVIDIA/cuopt/issues/new?template=submit-question.md)
- [All Issues](https://github.com/NVIDIA/cuopt/issues)