equivariant-architecture-designer

SKILL.md

This skill helps you design neural network architectures that respect identified symmetry groups. Given a validated group specification, it recommends architecture patterns, specific libraries, and implementation strategies.

The payoff: Equivariant architectures have fewer parameters, train faster, generalize better, and are more robust to distribution shift.

Gather the validated group specification. Confirm: which group(s) are involved, whether invariance or equivariance is needed, the data domain (images, point clouds, graphs, etc.), task type (classification, regression, generation), and any computational constraints. If group isn't specified, work with user to identify it first.

Use when you have validated symmetry groups and need to design neural network architecture that respects those symmetries. Invoke when user mentions equivariant layers, G-CNN, e3nn, steerable networks, building symmetry into model, or needs architecture recommendations for specific symmetry groups. Provides architecture patterns and implementation guidance. Source: lyndonkl/claude.

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