cuopt-numerical-optimization-api

Name: cuopt-numerical-optimization-api
Author: community

VERIFIED

by community

—No community reviews yet

84,780installs

Updated Jul 2026

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Description

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

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Install cuopt-numerical-optimization-api

Run this in your OpenClaw agent to add cuopt-numerical-optimization-api from the ClawHub registry.

terminal

$openclaw skills install cuopt-numerical-optimization-api

Requires ClawHub registry access. Review the security analysis below before installing.

Security Analysis

❌危险50/100

Open Source

Code is publicly available for audit.

Community Verified

Reviewed by the ClawHub community.

Community Reviews

Real user ratings only — separate from the editorial assessment and ClawHub signal.

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Related skills

cuopt-numerical-optimization-api-python

Frequently asked questions

Is cuopt-numerical-optimization-api safe to install?

cuopt-numerical-optimization-api has a SkillsReview security score of 50/100. It is open source and community-verified on ClawHub. Check the full Security Analysis on this page before installing.

How much does cuopt-numerical-optimization-api cost?

cuopt-numerical-optimization-api is free to install for OpenClaw via ClawHub.

What are the best alternatives to cuopt-numerical-optimization-api?

You can compare cuopt-numerical-optimization-api side by side with similar OpenClaw skills on the SkillsReview comparison page to find the best fit for your workflow.

How do I install cuopt-numerical-optimization-api?

Install cuopt-numerical-optimization-api from ClawHub at clawhub.ai/skills/cuopt-numerical-optimization-api, or use the install action on this page to copy the command for your OpenClaw agent.

Community Signal

ClawHub Community Score3.60 / 5.00

Installs84,780

Last UpdateJul 6, 2026

Actively maintained (0d ago)

ClawHub community score is a third-party marketplace signal. It is shown separately from SkillsReview editorial assessment and real user review averages.

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