cuopt-user-rules

Files (5)

• BENCHMARK.md
• SKILL.md
• evals.json · evals/
• skill-card.md
• skill.oms.sig

SKILL.md (first lines)

# 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:**

From sayalinvidia/sayali-skills-test, Apache-2.0.

More from sayalinvidia/sayali-skills-test