cuopt-installation-api-c

$npx mdskill add NVIDIA/skills/cuopt-installation-api-c

Install cuOpt C libraries and verify headers for GPU optimization.

  • Users need conda packages for C API installation and verification.
  • Skill requires NVIDIA drivers and CUDA 12.x or 13.x environments.
  • Agent executes find commands to locate headers and shared libraries.
  • Results display verification paths for libcuopt.so and cuopt_c.h files.

SKILL.md

.github/skills/cuopt-installation-api-cView on GitHub ↗
---
name: cuopt-installation-api-c
version: "26.06.00"
description: Install cuOpt for C — conda, locate lib/headers, verification. Use when the user is installing or verifying the C API. Standalone; no common skill.
---

# cuOpt Installation — C API (user)

Install cuOpt to *use* it from C. Standalone skill (no separate common).

## System requirements

- **GPU**: NVIDIA Compute Capability ≥ 7.0 (Volta+). CUDA 12.x or 13.x.
- **Driver**: Compatible NVIDIA driver. Python and C are separate installables.

## conda (C / libcuopt)

```bash
conda install -c rapidsai -c conda-forge -c nvidia cuopt
# libcuopt is provided by the same channel; Python and C are separate packages.
```

## Verify C API

```bash
find $CONDA_PREFIX -name "cuopt_c.h"
find $CONDA_PREFIX -name "libcuopt.so"
```

## Examples

- [verification_examples.md](resources/verification_examples.md) — C API verification

More from NVIDIA/skills

SkillDescription
accessing-mlflowQuery and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
ad-add-fusion-transformation>
ad-conf-check>
ad-graph-dump>
ad-model-onboard>
ad-pipeline-failure-pr>
add-benchmark>
aiq-deploy|
aiq-research|
byobCreate custom LLM evaluation benchmarks using the BYOB decorator framework. Use when the user wants to (1) create a new benchmark from a dataset, (2) pick or write a scorer, (3) compile and run a BYOB benchmark, (4) containerize a benchmark, or (5) use LLM-as-Judge evaluation. Triggers on mentions of BYOB, custom benchmark, bring your own benchmark, scorer, or benchmark compilation.