Multi-Objective Exploration

Multi-Objective Exploration is an Automation skill for Claude Code, published by NVIDIA in skills.

3K stars345 forkson NVIDIA/skillsAdded 2026/08/17+3% in starsRepository updated 2026/08/17

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Install in seconds

Install Multi-Objective Exploration

Copy Multi-Objective Exploration into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration ~/.claude/skills/cuopt-multi-objective-exploration

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/NVIDIA/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/cuopt-multi-objective-exploration/SKILL.md in NVIDIA/skills
Installs to: ~/.claude/skills/cuopt-multi-objective-exploration
Collection: One of 25 skills cataloged from this repository
Category: Automation — 1523 skills

What Multi-Objective Exploration does

Multi-Objective Exploration traces and interprets Pareto frontiers for multi-objective optimization problems by orchestrating repeated single-objective cuOpt solves (weighted-sum and ε-constraint). Use it when objectives conflict and the user needs the tradeoff curve, not a single weighted answer.

Multi-Objective Exploration is cataloged under Automation on DirSkills. Multi-Objective Exploration comes from a repository tagged agent-skills, agentic-ai, ai-agents, claude-code and codex.

Documentation

README

Excerpt

Multi-Objective Exploration

cuOpt optimizes one objective per solve. Many real problems have several objectives that pull against each other — cost vs. service level, return vs. risk, makespan vs. overtime, distance vs. vehicle count. A single solve answers "what's optimal for one particular weighting," but it hides the tradeoff the user actually needs to see.

This skill turns a sequence of single-objective cuOpt solves into a Pareto frontier — the set of solutions where you can't improve one objective without giving up another — and gives the discipline to read it. It adds no solver features; it orchestrates the LP / MILP / QP solves already covered by the formulation and API skills.

When this applies

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Multi-Objective Exploration

What else does NVIDIA publish alongside Multi-Objective Exploration?
Multi-Objective Exploration is one of 25 skills that DirSkills catalogs from NVIDIA/skills, the repository it ships in. Its siblings there include AIQ Deploy, AIQ Research and AMC RTSP Calibration. Each one is a separate skill with its own page in this directory, installs the same way Multi-Objective Exploration does, and is maintained by NVIDIA in that same repository. The rest of the collection is listed on the NVIDIA/skills page.
How does Multi-Objective Exploration compare to other Automation skills?
Multi-Objective Exploration ranks #406 by stars among the 1523 Automation skills in this catalog. The most-starred ones next to it are Autonomous Loops, Autonomous Agent Harness and Automation Audit Ops. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Multi-Objective Exploration against them. Open each page to compare what they document and how they install.