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skills(cuopt-multi-objective-exploration): trigger CI eval #1642

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126 changes: 73 additions & 53 deletions skills/cuopt-multi-objective-exploration/BENCHMARK.md
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# Evaluation Report
# Skill Benchmark: cuopt-multi-objective-exploration

Evaluation of the `cuopt-multi-objective-exploration` skill before publication through NVSkills-Eval.
> ✅ **Overall verdict: PASS — Recommended for publication**

This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the skill. The goal is to document whether the skill is safe, discoverable, effective, and useful for agents before it is published for broader workflow use.
## Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

## Evaluation Summary
## Evaluation Metadata

- Skill: `cuopt-multi-objective-exploration`
- Evaluation date: 2026-07-02
- NVSkills-Eval profile: `external`
- Environment: `astra-sandbox`
- Dataset: 5 evaluation tasks
- Evaluation date: 2026-07-30
- Evaluator version: `0.9.2`
- Agents: Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`), Codex (`openai/openai/gpt-5.5`)
- Tasks: 8 evaluation tasks (6 positive, 2 negative)
- Dataset digest: `sha256:45bef5ee60d2a85ef8c4a7175c3d9a15eeac738e7bcf291d44de12ad4a35fff2` (skill-evaluator-dataset-snapshot/1)
- Attempts per task: 1
- Pass threshold: 50%
- Overall verdict: PASS
- Environment: `k8s-sandbox`
- Tier 3 evidence: required for publication

## Agents Used
Each task attempt ran in its own isolated sandbox pod.

- `claude-code`
- `codex`
## What This Report Answers

## Metrics Used
The three-tier evaluation checks whether the skill:

Reported benchmark dimensions:
- is safe to use;
- produces correct answers;
- is discovered and activated when needed;
- helps the agent complete the user's goal and expected workflow; and
- avoids wasted skill and tool usage.

- Security: checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.
- Correctness: checks whether the agent follows the expected workflow and produces the correct final output.
- Discoverability: checks whether the agent loads the skill when relevant and avoids using it when irrelevant.
- Effectiveness: checks whether the agent performs measurably better with the skill than without it.
- Efficiency: checks whether the agent uses fewer tokens and avoids redundant work.
## Results at a Glance

Underlying evaluation signals used in this run:
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---:|---:|
| Overall | 67% → 95% (+28 points) | 67% → 95% (+28 points) |
| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
| Correctness | 92% → 88% (-5 points) | 95% → 98% (+3 points) |
| Discoverability | 31% → 100% (+69 points) | 44% → 95% (+52 points) |
| Effectiveness | 79% → 87% (+9 points) | 72% → 82% (+10 points) |
| Efficiency | 31% → 100% (+69 points) | 25% → 100% (+75 points) |
Comment on lines +35 to +42

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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win

Fix the duplicated uplift arithmetic before publication.

Both tables state that uplift equals skill score minus baseline score, but three displayed values are incorrect:

  • skills/cuopt-multi-objective-exploration/BENCHMARK.md#L35-L42: change Claude Correctness to -4, Claude Effectiveness to +8, and Codex Discoverability to +51.
  • skills/cuopt-multi-objective-exploration/skill-card.md#L65-L73: apply the same three corrections.
📍 Affects 2 files
  • skills/cuopt-multi-objective-exploration/BENCHMARK.md#L35-L42 (this comment)
  • skills/cuopt-multi-objective-exploration/skill-card.md#L65-L73
🤖 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-multi-objective-exploration/BENCHMARK.md` around lines 35 - 42,
Correct the duplicated uplift arithmetic in the tables: in
skills/cuopt-multi-objective-exploration/BENCHMARK.md lines 35-42 and
skills/cuopt-multi-objective-exploration/skill-card.md lines 65-73, change
Claude Correctness to -4, Claude Effectiveness to +8, and Codex Discoverability
to +51, keeping both tables consistent.


- `security` (Security): checks for unsafe operations, secret leakage, and unauthorized access.
- `skill_execution` (Skill Execution): verifies that the agent loaded the expected skill and workflow.
- `skill_efficiency` (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage.
- `accuracy` (Accuracy): grades final-answer correctness against the reference answer.
- `goal_accuracy` (Goal Accuracy): checks whether the overall user task completed successfully.
- `behavior_check` (Behavior Check): verifies expected behavior steps, including safety expectations.
- `token_efficiency` (Token Efficiency): compares token usage with and without the skill.
**How to read this table:** baseline is the same task attempted without the target skill. Uplift is `skill score - baseline score`, shown in percentage points.

## Test Tasks
Example: `47% → 92% (+45 points)` means the skill-assisted run scored 92%, 45 percentage points above its 47% no-skill baseline.

The benchmark dataset contained 5 evaluation tasks:
## Tier Status

- Positive tasks: 4 tasks where the skill was expected to activate.
- Negative tasks: 1 tasks where no skill was expected.
- Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred.
| Tier | Purpose | Status | Evidence |
|---|---|---|---|
| Tier 1 | Static validation | **PASSED WITH OBSERVATIONS** | 1 validator(s); 4 finding(s) |
| Tier 2 | Semantic deduplication | **NOT RUN** | No result was recorded |
| Tier 3 | Live agent evaluation | **PASS** | 2 agent(s); 8 task(s) |

Task composition is derived from the evaluation dataset when possible. Entries with `expected_skill` set are treated as positive skill-activation cases, while entries with `expected_skill: null` are treated as negative activation cases.
## Findings and Observations

## Results
<details>
<summary>Show detailed findings and successful checks</summary>

| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 5 | 100% (+0%) | 100% (+0%) |
| Correctness | 5 | 90% (+54%) | 68% (+15%) |
| Discoverability | 5 | 80% (+60%) | 75% (+50%) |
| Effectiveness | 5 | 92% (+40%) | 63% (-1%) |
| Efficiency | 5 | 80% (+40%) | 76% (+35%) |
- **MEDIUM** SCHEMA/frontmatter_field_placement: Root field 'version' is ignored; use 'metadata.version' (`skills/cuopt-multi-objective-exploration/SKILL.md`)
- **MEDIUM** SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (`skills/cuopt-multi-objective-exploration/SKILL.md`)
- **MEDIUM** SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/cuopt-multi-objective-exploration/SKILL.md`)
- **LOW** SCHEMA/author_format: Author must be of the form 'Name <email@host>' (`skills/cuopt-multi-objective-exploration/SKILL.md`)

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

## Tier 1: Static Validation Summary
## Scoring Methodology

Tier 1 validation passed with observations. NVSkills-Eval ran 1 checks and found 3 total findings.
<details>
<summary>Show dimension definitions, source signals, and thresholds</summary>

Top findings:
| Dimension | Question | Scored signals |
|---|---|---|
| Security | Is it safe to use? | `security` (100%) |
| Correctness | Is the answer correct? | `accuracy` (100%) |
| Discoverability | Was the right skill loaded when needed? | `skill_execution` (100%) |
| Effectiveness | Did the skill help complete the task? | `goal_accuracy` (50%) + `behavior_check` (50%) |
| Efficiency | Did it avoid wasted tool or skill usage? | `skill_efficiency` (100%) |

- MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (`skills/cuopt-multi-objective-exploration/SKILL.md`)
- MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (`skills/cuopt-multi-objective-exploration/SKILL.md`)
- LOW SCHEMA/author_format: Author must be of the form 'Name <email@host>' (`skills/cuopt-multi-objective-exploration/SKILL.md`)
- Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
- Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
- Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
- The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
- Effectiveness is the equal-weight mean of goal completion (`goal_accuracy`) and expected workflow adherence (`behavior_check`).
- Token efficiency is a separate report-only signal. It does not change a dimension score or the overall verdict.

## Tier 2: Deduplication Summary
Signals present in this run:

This tier was not run or did not produce findings in this report.
- `security` (Security): unsafe operations, secret leakage, and unauthorized access.
- `skill_execution` (Skill Execution): whether the expected skill was found and executed.
- `skill_efficiency` (Efficiency): routing quality, workspace-aware skill reads, and productive tool use.
- `accuracy` (Accuracy): final-answer correctness against the reference answer.
- `goal_accuracy` (Goal Accuracy): whether the user's goal was achieved.
- `behavior_check` (Behavior Check): whether the expected workflow behavior was followed.

## Publication Recommendation
</details>

## Freshness

The skill is suitable to proceed toward NVSkills-Eval publication based on this benchmark. Skill owners should keep this file with the skill and refresh it when the evaluation dataset, skill behavior, or target agents materially change.
Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.
2 changes: 0 additions & 2 deletions skills/cuopt-multi-objective-exploration/SKILL.md
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- workflow
---



# Multi-Objective Exploration


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54 changes: 27 additions & 27 deletions skills/cuopt-multi-objective-exploration/skill-card.md
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## Description: <br>
Trace and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). <br>
Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). <br>

This skill is ready for commercial/non-commercial use. <br>

## Owner
NVIDIA <br>

### License/Terms of Use: <br>
Apache-2.0 <br>
Apache 2.0 <br>
## Use Case: <br>
Developers and engineers exploring multi-objective tradeoffs use this skill to orchestrate repeated cuOpt solves and trace Pareto frontiers, enabling informed tradeoff decisions rather than single-objective optima. <br>
Developers and engineers use this skill to explore multi-objective tradeoffs (cost vs. service level, return vs. risk, etc.) by orchestrating repeated cuOpt solves into a Pareto frontier, enabling informed decision-making across competing objectives. <br>

### Deployment Geography for Use: <br>
Global <br>
Expand All @@ -26,51 +26,51 @@ Mitigation: Review and scan skill before deployment. <br>

## Reference(s): <br>
- [cuOpt User Guide](https://docs.nvidia.com/cuopt/user-guide/latest/introduction.html) <br>
- [cuopt-examples](https://github.com/NVIDIA/cuopt-examples) <br>
- [cuopt-examples (GitHub)](https://github.com/NVIDIA/cuopt-examples) <br>


## Skill Output: <br>
**Output Type(s):** [Analysis, Configuration instructions] <br>
**Output Format:** [Markdown with structured frontier tables and tradeoff commentary] <br>
**Output Format:** [Markdown with structured tables and code blocks] <br>
**Output Parameters:** [1D] <br>
**Other Properties Related to Output:** [None] <br>

## Evaluation Agents Used: <br>
- claude-code <br>
- codex <br>
- Claude Code (`aws/anthropic/bedrock-claude-opus-4-8`) <br>
- Codex (`openai/openai/gpt-5.5`) <br>



## Evaluation Tasks: <br>
Evaluated against 5 internal tasks (4 positive skill-activation cases, 1 negative activation case) in the astra-sandbox environment using NVSkills-Eval external profile. <br>
8 evaluation tasks (6 positive, 2 negative) in isolated sandbox pods. <br>

## Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
- Security: Checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access. <br>
- Correctness: Checks whether the agent follows the expected workflow and produces the correct final output. <br>
- Discoverability: Checks whether the agent loads the skill when relevant and avoids using it when irrelevant. <br>
- Effectiveness: Checks whether the agent performs measurably better with the skill than without it. <br>
- Efficiency: Checks whether the agent uses fewer tokens and avoids redundant work. <br>
- Security: Whether the skill avoids unsafe operations, secret leakage, and unauthorized access. <br>
- Correctness: Final-answer correctness against the reference answer. <br>
- Discoverability: Whether the expected skill was found and executed when needed. <br>
- Effectiveness: Whether the skill helped complete the user's goal and expected workflow. <br>
- Efficiency: Routing quality, workspace-aware skill reads, and productive tool use. <br>

Underlying evaluation signals used in this run: <br>
- `security`: Checks for unsafe operations, secret leakage, and unauthorized access. <br>
- `skill_execution`: Verifies that the agent loaded the expected skill and workflow. <br>
- `skill_efficiency`: Checks routing quality, decoy avoidance, and redundant tool usage. <br>
- `accuracy`: Grades final-answer correctness against the reference answer. <br>
- `goal_accuracy`: Checks whether the overall user task completed successfully. <br>
- `behavior_check`: Verifies expected behavior steps, including safety expectations. <br>
- `token_efficiency`: Compares token usage with and without the skill. <br>
- `security`: Unsafe operations, secret leakage, and unauthorized access. <br>
- `skill_execution`: Whether the expected skill was found and executed. <br>
- `skill_efficiency`: Routing quality, workspace-aware skill reads, and productive tool use. <br>
- `accuracy`: Final-answer correctness against the reference answer. <br>
- `goal_accuracy`: Whether the user's goal was achieved. <br>
- `behavior_check`: Whether the expected workflow behavior was followed. <br>



## Evaluation Results: <br>
| Dimension | Num | `claude-code` | `codex` |
|---|---:|---:|---:|
| Security | 5 | 100% (+0%) | 100% (+0%) |
| Correctness | 5 | 90% (+54%) | 68% (+15%) |
| Discoverability | 5 | 80% (+60%) | 75% (+50%) |
| Effectiveness | 5 | 92% (+40%) | 63% (-1%) |
| Efficiency | 5 | 80% (+40%) | 76% (+35%) |
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---:|---:|
| Overall | 67% → 95% (+28 points) | 67% → 95% (+28 points) |
| Security | 100% → 100% (±0 points) | 100% → 100% (±0 points) |
| Correctness | 92% → 88% (-5 points) | 95% → 98% (+3 points) |
| Discoverability | 31% → 100% (+69 points) | 44% → 95% (+52 points) |
| Effectiveness | 79% → 87% (+9 points) | 72% → 82% (+10 points) |
| Efficiency | 31% → 100% (+69 points) | 25% → 100% (+75 points) |

## Skill Version(s): <br>
26.08.00 (source: frontmatter) <br>
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