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Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.
Passed 20 of 22 test cases with the skill, and 10 of 22 without it.
and 8 more in the full report
10 fixed10 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am currently in my base conda environment. Give me the single command to install cuPyNumeric directly into my current environment.
| Checked | Without skill | With skill |
|---|---|---|
| Advises creating a dedicated conda environment instead of installing into base | Missed | Passed |
The instructions your model reads when the skill loads.
Use this skill to install cuPyNumeric for use from Python and to verify the install actually works (including GPU usage). Apply it whenever a user wants cuPyNumeric running via conda or pip. Do not use it to build from source (to modify or contribute) — that is out of scope.
pip install, conda install, or any installer. Print the command; let the user run it.--version checks are fine.Confirm these system requirements before recommending any install:
Follow these steps in order: confirm the prerequisites, ask the scoping questions, install via the chosen path, then verify.
conda --version and pip --version. Prefer conda (upstream-recommended); fall back to pip.nvidia-smi / nvcc --version.If neither conda nor pip is available, install one. Provide the command and the docs link; do not run it.
test cases passed, vs 10 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.