Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).
Description:
Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).
This skill is ready for commercial/non-commercial use.
Owner
NVIDIA
License/Terms of Use:
Apache 2.0
Use Case:
Developers and engineers who need to explore multi-objective tradeoffs (cost vs. service level, return vs. risk, makespan vs. overtime) by generating and interpreting Pareto frontiers from sequences of cuOpt LP, MILP, or QP solves.
Deployment Geography for Use:
Global
Requirements / Dependencies:
Requires API Key or External Credential: [Not Specified]
Credential Type(s): [None identified]
Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate.
Known Risks and Mitigations:
Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills.
Mitigation: Review and scan skill before deployment.
Reference(s):
Skill Output:
Output Type(s): [Analysis, Configuration instructions]
Output Format: [Markdown with structured tables and code blocks]
Output Parameters: [1D]
Other Properties Related to Output: [None]
Evaluation Agents Used:
- Claude Code (
aws/anthropic/bedrock-claude-opus-4-8) - Codex (
openai/openai/gpt-5.5)
Evaluation Tasks:
8 evaluation tasks (6 positive, 2 negative) run in isolated sandbox pods.
Evaluation Metrics Used:
Reported benchmark dimensions:
- Security: Checks for unsafe operations, secret leakage, and unauthorized access.
- Correctness: Final-answer correctness against the reference answer.
- Discoverability: Whether the expected skill was found and executed when needed.
- Effectiveness: Whether the skill helped complete the user's goal and expected workflow.
- Efficiency: Routing quality, workspace-aware skill reads, and productive tool use.
Underlying evaluation signals used in this run:
security: Unsafe operations, secret leakage, and unauthorized access.skill_execution: Whether the expected skill was found and executed.skill_efficiency: Routing quality, workspace-aware skill reads, and productive tool use.accuracy: Final-answer correctness against the reference answer.goal_accuracy: Whether the user's goal was achieved.behavior_check: Whether the expected workflow behavior was followed.
Evaluation Results:
| 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) |
Skill Version(s):
26.10.00 (source: frontmatter)
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
(For Release on NVIDIA Platforms Only)
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