Agent skill · Backend & API

cuopt-server-api-python

cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.

NVIDIAgithub.com/NVIDIAGitHub ↗
claude-codecodexships scriptsApache-2.0
Install
npx skills add NVIDIA/skills --skill cuopt-server-api-python --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 16
SKILL.md size: 3 KB
Bundled scripts: yes
Version: 26.08.00
Declared author: NVIDIA cuOpt Team
Path: skills/cuopt-server-api-python/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,789
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# cuOpt Server — Deploy and client (Python/curl) This skill covers **starting the server** and **client examples** (curl, Python). Server has no separate C API (clients can be any language). ## Problem types supported | Problem type | Supported | |--------------|:---------:| | Routing | ✓ | | LP | ✓ | | MILP | ✓ | | QP | ✗ | ## Required questions Ask these if not already clear: 1. **Problem type**

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About this skill
What does the cuopt-server-api-python skill do?

cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.

How do I install it?

Run `npx skills add NVIDIA/skills --skill cuopt-server-api-python --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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