Agent skill · nvidia
cuequivariance-torch
Use when building or debugging cuEquivariance PyTorch tensor products or equivariant GNNs, especially CPU references, layouts, indexing, and gradients.
What it needs
About 9k tokens when loaded.
What this skill does
cuEquivariance in PyTorch Purpose Execute cuequivariance (cue) descriptors with cuequivariancetorch (cuet): CPU references, CUDA backends, equivariant layers, and autograd. Preserve the project's environment. Inputs Required inputs: operation, SO3/O3 irreps, layout, device, dtype, and weight ownership; infer from the request or code. Graphs also need source/destination counts and indices. Optional inputs: backend, performance, export targets. Instructions 1. Probe once and reuse working PyTorch. Never recursively delete directories during setup or recovery, including partial temporary environments; use a fresh unique path when isolation is needed. 2. Set method="naive" at construction for CPU. Match parameters, features, weights, and references in device and dtype. Use float64 for small numerical and gradient checks. 3. Specify layout and weight ownership explicitly. Derive sizes and output order from the module/descriptor; do not guess multiplicities from a filter. 4. Finish installation before running the saved script with the completion recipe below. A session/task ID, empty output, or a file write is not completion. Poll that session until it exits and inspect its results. 5. For an API mismatch, inspect the installed signature/source once. Adapt or explain the requirement; preserve the requested library. Prerequisites and environment reuse Probe imports independently with the project's Python; a missing frontend must not hide working PyTorch: Choose the next step from the probe: Imports work: retain the interpreter; install nothing. Only cuEquivariance is missing: install cuequivariance-torch in that interpreter; it does not require replacing PyTorch. PyTorch is missing: check the project interpreter (e.g. .venv/bin/python). If the requested installation is inaccessible, say so; a new environment cannot verify it. Binary/shared-library mismatch: diagnose the reported package/path and compatibility. Use one interpreter for pip and execution. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-cuequivariance-torch