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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Use this Skill: https://skilld.dev/gh/nvidia/skills/cuopt-numerical-optimization-api
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≈125 tokens on demand. Your agent reads this file only when SKILL.md points to it.
Retrieve dual values (shadow prices) and reduced costs after solving an LP.
Problem: Minimize 3x + 2y + 5z subject to x + y + z = 4, 2x + y + z = 5, x, y, z ≥ 0.
Build: With cuOpt on INCLUDE_PATH and LIB_PATH:
gcc -I${INCLUDE_PATH} -L${LIB_PATH} -o lp_duals lp_duals.c -lcuopt
LD_LIBRARY_PATH=${LIB_PATH}:$LD_LIBRARY_PATH ./lp_dualsSee also: references/examples.md for full parameter reference.
Source: SKILL.md on GitHub
The skill provides a comprehensive environment for modeling and solving optimization problems using the NVIDIA cuOpt library. It includes well-documented examples and reference implementations for Python, C, and CLI interfaces. Security analysis identified a remote data download from a reputable academic source and standard data ingestion patterns for optimization files, neither of which present malicious risk.
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Risk: MEDIUM · 1 issue
Signed by skilld at e0cd22d. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.
Last checked against GitHub yesterday.
{
"author": "NVIDIA cuOpt Team",
"tags": [
"cuopt",
"linear-programming",
"milp",
"qp",
"python",
"c-api",
"cli"
]
}