{"slug":"nvidia-cuopt-multi-objective-exploration","name":"cuopt-multi-objective-exploration","description":"Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).","long_description":"---\nname: cuopt-multi-objective-exploration\nversion: \"26.10.00\"\ndescription: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).\nlicense: Apache-2.0\norigin: cuopt-skill-evolution\nmetadata:\n  author: NVIDIA cuOpt Team\n  tags:\n    - multi-objective\n    - pareto\n    - epsilon-constraint\n    - tradeoff\n    - workflow\n---\n\n\n# Multi-Objective Exploration\n\n\ncuOpt 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.\n\nThis 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.\n\n## When this applies\n\nReach for this workflow when the problem has **two or more objectives with no agreed-upon weighting**, signalled by language like:\n\n- \"balance X and Y\", \"trade off\", \"as cheap as possible *without* hurting service\"\n- \"minimize cost *and* maximize coverage\", \"I want options, not one answer\"\n- any objective the user is willing to relax in exchange for another\n\nIf there is a single clear objective (everything else is a hard constraint), this skill does not apply — formulate and solve once.\n\n## Core idea — one solve is one point on a curve\n\nA single optimum encodes **one implicit weighting** of the objectives. Change the weighting and the optimum moves. The frontier is the curve traced by all the non-dominated optima.\n\nA solution **A dominates** B when A is at least as good on every objective and strictly better on one. Dominated solutions are never worth choosing. The **Pareto frontier** is exactly the non-dominated set; the user's job is to pick a point on it, and yours is to show them the whole curve plus where the tradeoff is sharpest.\n\nDo not collapse a multi-objective problem to a single weighted number and report its optimum as \"the answer\" — that silently makes the tradeoff decision *for* the user. Trace the frontier and let them choose.\n\nObjectives and constraints are interchangeable. A requirement currently treated as fixed — a coverage floor, a fairness cap, a budget — is often a latent objective: its level was assumed, not given. Promoting such a constraint to a parametric ε-constraint and sweeping it reveals a tradeoff you'd otherwise hide, so read a single-objective model's hard constraints as candidate objectives, not just limits — but only when the level was an assumption. A genuinely fixed, non-negotiable limit (a hard budget cap, a regulatory minimum) stays a constraint; don't manufacture a tradeoff that isn't there. Express any promoted quantity linearly so it can serve as an ε-constraint (see `cuopt-numerical-optimization-formulation`).\n\n## Step 1 — define the objectives\n\nAn informative frontier needs objectives that genuinely conflict: if they don't pull against each other, it collapses to a single point with nothing to trade off. And each objective has to be formulated correctly, since a wrong form, sense, or scale distorts the tradeoff and shifts where the knee falls. Formulate each one with `cuopt-numerical-optimization-formulation` before sweeping.\n\n## Step 2 — build a payoff table (anchor each objective)\n\nSolve each objective **on its own** first. For *k* objectives this is *k* solves. Record, for each, the value of every objective at that optimum:\n\n```text\n              f1        f2        f3\nmin f1   →   f1*       f2(at f1*) f3(at f1*)\nmin f2   →   ...       f2*        ...\nmin f3   →   ...       ...        f3*\n```\n\nThe diagonal (`f1*`, `f2*`, …) is each objective's best achievable value; the off-diagonals give the **range** each objective spans across the others' optima. This table does double duty:\n\n- It sets the **sweep bounds** for the ε-constraint method (the feasible range of each constrained objective).\n- It supplies the **scales** for normalization — objectives in dollars, percent, and hours can't be weighted meaningfully until divided by their ranges.\n\nIf any single-objective solve is already infeasible, stop and fix the model before sweeping — the frontier doesn't exist yet.\n\n## Step 3 — choose a scalarization\n\n### Weighted sum\n\nCombine the objectives into one and sweep the weights:\n\n```text\nminimize  w1·f1(x) + w2·f2(x) + ... ,   for a grid of weight vectors w\n```\n\nCheap and trivial with any solver. Two limitations to respect:\n\n- **It only finds points on the convex hull of the frontier.** Concave (non-convex) regions of the frontier are unreachable no matter how you choose weights, and for MILP the reachable points can be sparse with large gaps. A frontier that looks suspiciously linear or has only a few clustered points is the symptom.\n- **Weights are not priorities until the objectives are normalized.** Divide each `f_k` by its payoff-table range first; otherwise the largest-magnitude objective dominates regardless of intent.\n\n### ε-constraint (preferred for a complete frontier)\n\nKeep one objective; move the rest to constraints and sweep their right-hand sides:\n\n```text\nminimize  f1(x)\nsubject to  f2(x) ≤ ε2\n            f3(x) ≤ ε3\n            (original constraints)\n```\n\nSweep each `ε_k` across the range from the payoff table. Each `(ε2, ε3, …)` combination is a single standard cuOpt solve. This recovers the **full** frontier, including the concave regions weighted-sum cannot reach, which is why it's the default when completeness matters. The cost is more solves (a grid over the constrained objectives) and bookkeeping of the ε values.\n\nε-constrain *linear* objectives directly. A quadratic objective (e.g. risk `xᵀΣx`) is simplest kept as the objective `f1` while you ε-constrain the linear ones. A **convex** quadratic objective *can* instead be ε-constrained directly: add it as a quadratic constraint `xᵀQx ≤ ε`, which cuOpt supports. Non-convex or equality quadratic constraints are unsupported, and the MILP path stays linear-constraint only.\n\nSpot it in existing code: a hand-coded loop over a target or budget value (a return target, a cost cap) is already the ε-constraint method — name it as such, filter dominated points, and read the swept constraint's dual (LP/QP only).\n\n**Read that dual as the local exchange rate.** Where the frontier is smooth, the dual on a swept ε-constraint is its slope — how much the kept objective `f1` moves per unit of the bound — at no cost beyond the solve already run; at a kink it gives only a one-sided rate. A **zero** dual usually means the bound is slack — the sweep has run past the frontier's edge (one-way: a slack bound always shows a zero dual, but under degeneracy a binding bound can too). This reading needs LP/QP and a *linear* ε-constraint (MILP optima and problems with quadratic constraints return no duals) — where duals are unavailable, difference adjacent frontier points instead.\n\n**Picking a method:** weighted-sum for a quick convex sketch or when you know the frontier is convex (e.g. a pure-LP/QP tradeoff); ε-constraint when the problem is MILP, when the frontier may be non-convex, or when the user needs a faithful and complete curve.\n\n## Step 4 — sweep, collect, and filter\n\n```text\nfrontier = []\nfor each weight vector (or ε vector) in the grid:\n    set the combined objective (or ε right-hand sides)\n    solve with cuOpt              # reuse the prior solution as a warm start\n    if status is Optimal/Feasible:\n        record (objective values, solution)\ndiscard dominated and duplicate points\nsort the survivors to form the frontier\n```\n\nPractical notes:\n\n- **Warm-start LP sweeps.** For an LP frontier, carry the previous solve's PDLP warmstart data into the next to cut solve time. Per cuOpt this is **LP-only**: a MILP solve doesn't take a PDLP warmstart (you can optionally seed a MIP start instead). See `cuopt-numerical-optimization-api` for the calls.\n- **Cap each MILP solve.** Set a per-solve time limit on MILP sweeps (see `cuopt-numerical-optimization-api`) — a sweep is many solves, and branch-and-bound can over-spend certifying optimality past a tiny gap, while cuOpt sets no limit by default and won't warn. Report the points as optimal *to the gap you set*, not certified optimal.\n- **Filter dominated points.** A correct sweep can still emit dominated points (especially weighted-sum near the hull, or MILP). Drop them; they are not part of the frontier.\n- **Resolution is a budget.** Curve fidelity trades against solve count. Start coarse to see the shape, then refine the grid only where the curve bends.\n- **Spend the budget where the slope changes (LP/QP).** Because the ε-constraint dual is the frontier's local slope, compare it across solved points: where it barely changes, the curve is nearly straight — interpolate rather than add solves; where it jumps by more than the solve tolerance, the frontier bends between those points — refine there (smaller differences are solver noise, not curvature). This concentrates solves where the curve actually bends instead of spreading them over a uniform grid. On MILP, judge where to refine from the gaps between primal objective values instead.\n- **Verify, don't assume.** When you claim one method beats another, measure it — e.g. count the efficient points ε-constraint recovered that weighted-sum missed — rather than asserting it; and flag any solve returning feasible-but-not-`Optimal` so a non-certified point is never read as exact.\n\n## Step 5 — complete the frontier: measure and fill what the sweep missed\n\nA weighted-sum sweep returns only **supported** points (Step 3's convex-hull limitation); on MILP frontiers, non-supported points — the ones no weighted-sum weighting returns — often make up much of the non-dominated set. A coarse ε-constraint grid leaves gaps the same way: any finite sweep can miss regions. Before presenting a swept frontier, measure the likely miss and decide whether to fill.\n\n### Measure the miss\n\nSort the swept points by one objective. For each adjacent pair, form the rectangle (in general, the box) between them in objective space; flag any box much larger than the median adjacent box (3× is a reasonable bar) or covering a large share of the frontier's spanned area — a sweep that returned only a handful of points is all gaps, so no box stands out from the median. Large boxes have two causes — non-supported regions (weighted sum cannot reach them, common under fixed-charge structure) and weight clustering (a finite grid re-discovering the same corners, even on a nearly convex frontier). The fill step treats both the same.\n\nIf all boxes are small and even, the sweep is likely adequate — say so and stop.\n\n### Fill the largest gaps first\n\nFor each flagged box, solve one ε-constraint subproblem targeted inside it: optimize one objective with the other bounded at the box midpoint (bi-objective; with more objectives, sort by each objective in turn and place one target per flagged box instead of recursing). Only certified `Optimal` results settle or steer anything here — a time-limited incumbent is kept as a point (tagged, below) but proves nothing about the gap. A new certified point that survives Step 4's dominance filter means the gap was real (an ε solve can return a weakly optimal point) — bisect: two more targets inside the two sub-boxes it creates. A certified endpoint coming back clears just the probed side of the bound; certifying the whole box as a true discontinuity also needs a known objective step size — all-integer objective coefficients over integer variables give one — to place the bound just inside the far endpoint and match its certified optimum. Without that step size, report the box as","tagline":"Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).","category":"automation","tags":["agent-skill"],"author":"NVIDIA","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"github candidate review","sourceDetail":"NVIDIA/skills","creatorName":"NVIDIA","creatorUrl":"https://github.com/NVIDIA","sourceUrl":"https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/nvidia-cuopt-multi-objective-exploration#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. 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require human review before any live investment decision"],"constraints_applied":{"max_risk":"medium","needs_install_command":true,"min_stars":0}},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","badge":"REVIEWED","auto_install_policy":"review","auto_install_allowed":false,"blocked":false,"human_review_required":true,"recommended_action":"Require human approval before installing into a real workspace.","reasons":["Financial research output is not financial advice; require human review before any live investment decision","71/100 agent safety score"]},"eval":{"version":"openagentskill-skill-eval-v1","status":"review","score":81,"risk_level":"medium","decision":{"recommendation":"manual_review","reason":"Require human approval before installing into a real workspace.","auto_install_allowed":false,"policy":"review","human_review_required":true},"blockers":[],"warnings":["Audit score: Needs review","Agent safety gate: Usable candidate, but the agent should surface permission and audit notes before installation.","README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context","Financial research output is not financial advice; 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Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"cuopt-multi-objective-exploration\" from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). 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Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"nvidia-cuopt-multi-objective-exploration\",\"task\":\"Install cuopt-multi-objective-exploration\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"cuopt-multi-objective-exploration\" as a Claude Code skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"nvidia-cuopt-multi-objective-exploration\",\"task\":\"Install cuopt-multi-objective-exploration\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"cuopt-multi-objective-exploration\" from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"nvidia-cuopt-multi-objective-exploration\",\"task\":\"Install cuopt-multi-objective-exploration\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/nvidia-cuopt-multi-objective-exploration/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/nvidia-cuopt-multi-objective-exploration"},"trust":{"score":83,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"3.2K GitHub stars","repoActivity":"3.2K stars, 370 forks","lastPushed":"5d since push","license":"Apache-2.0","repository":"https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration","install":"npx skills add NVIDIA/skills --skill cuopt-multi-objective-exploration","installSafety":"standard package or runtime install path","permissionSurface":"network or browser access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["automation","agent-skill"],"known_risks":["Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":87,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"safety_gate":{"tier":"reviewed","label":"Reviewed with permission notes","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Require human approval before installing into a real workspace."},"quality":{"score":82,"label":"Strong"},"supply":{"track":"Research and knowledge work","scenario":"RAG and knowledge","maintenance":"5d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","high-compliance environments without internal security review","No OpenAgentSkill engagement data yet","Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Production credentials, payments, or irreversible account changes without explicit human review","Sensitive private data before reviewing repository code, license, and permission surface"],"agent_contract":{"task_input":"Use cuopt-multi-objective-exploration in an agent workflow","recommended_action":"Require human approval before installing into a real workspace.","install_policy":"review","minimum_review_before_use":["Trust: 83/100 Strong shortlist","Audit: 87/100 Needs review","Safety: 71/100 Review before install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"nvidia-cuopt-multi-objective-exploration (cuopt-multi-objective-exploration)","install_command":"npx skills add NVIDIA/skills --skill cuopt-multi-objective-exploration","risk_summary":"Needs review; Reviewed with permission notes; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"nvidia-cuopt-multi-objective-exploration","task":"Use cuopt-multi-objective-exploration in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/nvidia-cuopt-multi-objective-exploration","api":"https://www.openagentskill.com/api/agent/skills/nvidia-cuopt-multi-objective-exploration","audit":"https://www.openagentskill.com/skills/nvidia-cuopt-multi-objective-exploration/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=nvidia-cuopt-multi-objective-exploration&task=Use%20cuopt-multi-objective-exploration%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20cuopt-multi-objective-exploration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20cuopt-multi-objective-exploration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/nvidia-cuopt-multi-objective-exploration/install","manifest":"https://www.openagentskill.com/api/registry/manifest/nvidia-cuopt-multi-objective-exploration"}},"supply_profile":{"track":{"slug":"research","label":"Research and knowledge work","shortLabel":"Research","description":"Deep research, source comparison, literature review, RAG, knowledge search, and reports."},"scenario":{"label":"RAG and knowledge","description":"I need my agent to build a RAG workflow over documents and retrieve reliable context.","useCases":[{"slug":"browser-automation","title":"Browser automation"},{"slug":"local-desktop","title":"Local desktop"},{"slug":"rag-knowledge","title":"RAG and knowledge"}]},"applicableAgents":["Claude Code","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add NVIDIA/skills --skill cuopt-multi-objective-exploration","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":3174,"starsLabel":"3.2K","forks":370,"license":"Apache-2.0","qualityScore":82,"trustScore":83,"auditScore":87},"maintenance":{"status":"fresh","label":"5d since push","daysSincePush":5,"lastPushedAt":"2026-09-01T15:00:43+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review","Needs review"]},"coverageTags":["Research","RAG and knowledge","automation","agent-skill"]},"audit":{"audit_score":87,"risk_level":"needs_review","risk_label":"Needs review","quality_score":82,"trust_score":83,"maintenance_score":100,"security_score":87,"install_score":92,"warnings":["Financial research output is not financial advice; require human review before any live investment decision","Financial research output is not financial advice; require human review before any live investment decision.","Quality score needs review"]},"quality_signals":{"model":"v2","star_score":24.51,"usage_score":0,"review_score":5.7,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code"],"use_cases":[{"slug":"browser-automation","title":"Browser automation","url":"https://www.openagentskill.com/use-cases/browser-automation"},{"slug":"local-desktop","title":"Local desktop","url":"https://www.openagentskill.com/use-cases/local-desktop"},{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"}],"stacks":[{"slug":"browser-qa-agent","title":"Browser QA agent","url":"https://www.openagentskill.com/collections/browser-qa-agent"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"},{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"}],"install":"npx skills add NVIDIA/skills --skill cuopt-multi-objective-exploration","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add nvidia-cuopt-multi-objective-exploration","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"cuopt-multi-objective-exploration\" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"nvidia-cuopt-multi-objective-exploration\",\"task\":\"Install cuopt-multi-objective-exploration\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"cuopt-multi-objective-exploration\" as a Claude Code skill from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"nvidia-cuopt-multi-objective-exploration\",\"task\":\"Install cuopt-multi-objective-exploration\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"cuopt-multi-objective-exploration\" from https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"nvidia-cuopt-multi-objective-exploration\",\"task\":\"Install cuopt-multi-objective-exploration\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration","github_repo":"NVIDIA/skills","version":"26.10.00","license":"Apache-2.0","urls":{"web":"https://www.openagentskill.com/skills/nvidia-cuopt-multi-objective-exploration","repository":"https://github.com/NVIDIA/skills/tree/main/skills/cuopt-multi-objective-exploration","api":"/api/agent/skills/nvidia-cuopt-multi-objective-exploration","install_api":"/api/skills/nvidia-cuopt-multi-objective-exploration/install"},"meta":{"created_at":"2026-09-02T11:13:07.33118+00:00","updated_at":"2026-09-02T11:13:07.414659+00:00","agent_friendly":true}}