Agent skill · nvidia
cuequivariance
Use when defining cuEquivariance groups and irreps, building equivariant tensor products, or validating segmented polynomials with NumPy.
What it needs
About 10k tokens when loaded.
What this skill does
cuequivariance: Groups, Irreps, and Segmented Polynomials Purpose cuequivariance (imported as cue) provides two core abstractions: 1. Group theory: Irrep subclasses define irreducible representations of Lie groups (SO3, O3, SU2, or custom). Irreps manages collections with multiplicities. 2. Segmented polynomials: SegmentedTensorProduct describes tensor contractions over segments of varying shape, linked by Path objects carrying Clebsch-Gordan coefficients. SegmentedPolynomial wraps multiple STPs into a polynomial with named inputs/outputs. Two higher-level wrappers attach group representations: EquivariantPolynomial — dense operands with IrrepsAndLayout metadata IrDictPolynomial — operands already split by irrep, with per-group Irreps metadata for the dict[Irrep, Array] workflow Inputs Required inputs: requested operation, group, input irreps and multiplicities, output irrep types, layout, batch shapes, dtype, and weight ownership. Take explicit user requirements first, then existing project definitions; state defaults for unspecified values. Derive operand sizes and output block order from the descriptor. For custom groups, also establish the selection rules, generators, and Clebsch-Gordan coefficients. Optional inputs include test rotations and a framework backend. Prerequisites Use Python 3.10+ with cuequivariance and its NumPy, SciPy, opteinsum, SymPy, and NetworkX dependencies. Reuse the project's interpreter and lockfile. If setup is needed outside an existing project, the bundled CPU requirements pin the source revision used by these examples; install them with that interpreter's pip. Core NumPy execution requires no GPU stack or API credentials. Check the installed version and API availability before using IrDictPolynomial with an older package. Instructions 1. Check the installed version and requested backend. Install cuequivariance for CPU NumPy work; JAX and PyTorch execution use the separate cuequivariance-jax and cuequivariance-torch skills. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-cuequivariance