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ramakrishnap-nv merged 2 commits into from
Jun 24, 2026
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ci: trigger numerical optimization skill evals #1453

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ci: trigger numerical optimization formulation skill eval
ramakrishnap-nv Jun 23, 2026
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Attach NVSkills validation signatures
svc-nvskills-signing Jun 23, 2026
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10 changes: 5 additions & 5 deletions skills/cuopt-numerical-optimization-formulation/BENCHMARK.md
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Expand Up @@ -7,7 +7,7 @@ This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the s
## Evaluation Summary

- Skill: `cuopt-numerical-optimization-formulation`
- Evaluation date: 2026-06-22
- Evaluation date: 2026-06-23
- NVSkills-Eval profile: `external`
- Environment: `astra-sandbox`
- Dataset: 3 evaluation tasks
Expand Down Expand Up @@ -55,10 +55,10 @@ Task composition is derived from the evaluation dataset when possible. Entries w
| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 3 | 100% (+0%) | 100% (+0%) |
| Correctness | 3 | 67% (+20%) | 33% (-3%) |
| Discoverability | 3 | 33% (+33%) | 0% (+0%) |
| Effectiveness | 3 | 89% (+2%) | 69% (+1%) |
| Efficiency | 3 | 49% (+22%) | 28% (-0%) |
| Correctness | 3 | 83% (+33%) | 33% (-7%) |
| Discoverability | 3 | 67% (+67%) | 0% (+0%) |
| Effectiveness | 3 | 98% (+5%) | 67% (-11%) |
| Efficiency | 3 | 72% (+45%) | 28% (-0%) |

Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.

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1 change: 1 addition & 0 deletions skills/cuopt-numerical-optimization-formulation/SKILL.md
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Expand Up @@ -13,6 +13,7 @@ metadata:
- concepts
---


# Numerical Optimization Formulation

Concepts and workflow for going from a problem description to a clear formulation across LP, MILP, and QP. No API code here.
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25 changes: 15 additions & 10 deletions skills/cuopt-numerical-optimization-formulation/skill-card.md
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Expand Up @@ -9,7 +9,7 @@ NVIDIA <br>
### License/Terms of Use: <br>
Apache 2.0 <br>
## Use Case: <br>
Developers and engineers formulating linear, mixed-integer, and quadratic optimization problems with NVIDIA cuOpt, parsing unstructured problem statements into structured mathematical formulations. <br>
Developers and engineers use this skill to parse optimization problem descriptions and formulate them as LP, MILP, or QP models with correct parameters, constraints, decision variables, and objectives before solving with cuOpt. <br>

### Deployment Geography for Use: <br>
Global <br>
Expand All @@ -24,19 +24,19 @@ Mitigation: Review and scan skill before deployment. <br>


## Skill Output: <br>
**Output Type(s):** [Analysis] <br>
**Output Format:** [Markdown] <br>
**Output Type(s):** [Analysis, Code] <br>
**Output Format:** [Markdown with mathematical formulations] <br>
**Output Parameters:** [1D] <br>
**Other Properties Related to Output:** [None] <br>

## Evaluation Agents Used: <br>
- claude-code <br>
- codex <br>
- `claude-code` <br>
- `codex` <br>



## Evaluation Tasks: <br>
Evaluated against 3 internal skill-activation tasks (NVSkills-Eval, external profile, astra-sandbox environment). <br>
Evaluated against 3 internal evaluation tasks (all positive skill-activation cases) via NVSkills-Eval external profile. <br>

@coderabbitai coderabbitai Bot Jun 23, 2026

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🎯 Functional Correctness | 🟠 Major | ⚡ Quick win

Discoverability is over-claimed with positive-only tasks.

Line 39 says all 3 evaluation tasks are positive activation cases, but discoverability (Line 45) also includes “avoid using it when irrelevant.” That irrelevant-case behavior is not measurable from positive-only tasks, so the discoverability result is methodologically incomplete.

Suggested doc fix 
-Evaluated against 3 internal evaluation tasks (all positive skill-activation cases) via NVSkills-Eval external profile. <br>
+Evaluated against 3 internal evaluation tasks (all positive skill-activation cases) via NVSkills-Eval external profile. In this run, Discoverability reflects relevant-case activation only (no negative-control tasks). <br>
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
Evaluated against 3 internal evaluation tasks (all positive skill-activation cases) via NVSkills-Eval external profile. <br>
Evaluated against 3 internal evaluation tasks (all positive skill-activation cases) via NVSkills-Eval external profile. In this run, Discoverability reflects relevant-case activation only (no negative-control tasks). <br>
🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@skills/cuopt-numerical-optimization-formulation/skill-card.md` at line 39,
The discoverability claim on Line 45 states the skill should avoid being used
when irrelevant, but Line 39 only documents evaluation against positive
skill-activation cases. To fix this methodological gap, either add negative test
cases to the evaluation and update Line 39 to reflect the expanded test set, or
narrow the discoverability description on Line 45 to only claim measurable
behavior from positive-only testing (such as only claiming successful activation
in relevant contexts, without claiming avoidance of irrelevant use cases).


## Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
Expand All @@ -61,10 +61,15 @@ Underlying evaluation signals used in this run: <br>
| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 3 | 100% (+0%) | 100% (+0%) |
| Correctness | 3 | 67% (+20%) | 33% (-3%) |
| Discoverability | 3 | 33% (+33%) | 0% (+0%) |
| Effectiveness | 3 | 89% (+2%) | 69% (+1%) |
| Efficiency | 3 | 49% (+22%) | 28% (-0%) |
| Correctness | 3 | 83% (+33%) | 33% (-7%) |
| Discoverability | 3 | 67% (+67%) | 0% (+0%) |
| Effectiveness | 3 | 98% (+5%) | 67% (-11%) |
| Efficiency | 3 | 72% (+45%) | 28% (-0%) |

@coderabbitai coderabbitai Bot Jun 23, 2026

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📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick win

Normalize delta formatting to avoid -0%.

Line 67 shows 28% (-0%), which is confusing in a published benchmark table. Use 0% for zero deltas.

Suggested doc fix 
-| Efficiency | 3 | 72% (+45%) | 28% (-0%) |
+| Efficiency | 3 | 72% (+45%) | 28% (0%) |
🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@skills/cuopt-numerical-optimization-formulation/skill-card.md` at line 67,
The Efficiency row contains a confusing delta value of (-0%) for the third
column which should be normalized to (0%) for consistency and clarity in the
benchmark table. Locate the Efficiency row that shows 72% (+45%) and update the
corresponding delta value from (-0%) to (0%) to maintain uniform formatting
across all delta values.


## Testing Completed: <br>
**[x] Agent Red-Teaming** <br>
**[ ] Network Security** <br>
**[ ] Product Security** <br>

## Skill Version(s): <br>
26.08.00 (source: frontmatter) <br>
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2 changes: 1 addition & 1 deletion skills/cuopt-numerical-optimization-formulation/skill.oms.sig
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