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/cuopt-multi-objective-exploration

@b3123b1
by NVIDIA Corporationnvidia/skills3.5k stars
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Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).

Use this Skill: https://skilld.dev/gh/nvidia/skills/cuopt-multi-objective-exploration

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BENCHMARK.md

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Skill Benchmark: cuopt-multi-objective-exploration

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

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

Evaluation Metadata

  • Skill: cuopt-multi-objective-exploration
  • Evaluation date: 2026-08-05
  • Evaluator version: 1.0.0
  • 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
  • Environment: k8s-sandbox
  • Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

  • 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.

Results at a Glance

Measure Claude Code (Baseline → Skill Uplift) Codex (Baseline → Skill Uplift)
Overall 65% → 97% (+33 points) 67% → 90% (+23 points)
Security 100% → 100% (±0 points) 100% → 100% (±0 points)
Correctness 95% → 100% (+5 points) 95% → 100% (+5 points)
Discoverability 25% → 100% (+75 points) 44% → 84% (+40 points)
Effectiveness 79% → 87% (+8 points) 73% → 85% (+12 points)
Efficiency 25% → 100% (+75 points) 25% → 83% (+58 points)

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.

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

Tier Status

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)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>
  • 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)
</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>
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%)
  • 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.

Signals present in this run:

  • 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.
</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub1mo

    This skill is a documentation-only resource providing guidance on multi-objective optimization using NVIDIA cuOpt. It contains no executable scripts, no sensitive data access, and no obfuscated content. All external references target official NVIDIA domains and repositories.

  • Socket1mo

    No alerts

  • Snyk1mo

    Risk: LOW · No issues

Signed by skilld at b3123b1. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 months ago
version
26.10.00
origin
cuopt-skill-evolution
Other metadata
metadata
{
  "author": "NVIDIA cuOpt Team",
  "tags": [
    "multi-objective",
    "pareto",
    "epsilon-constraint",
    "tradeoff",
    "workflow"
  ]
}

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