How does it handle approximate symmetries?

It analyzes data distribution under transforms and provides an interpretation guide to help you decide between hard constraints, soft regularization, or data augmentation.

When should I use the Symmetry Validation Suite?

Use it during the model design phase to confirm if hypothesized symmetries like rotation or translation hold in your data before building an equivariant model.

Can it verify mathematical group properties?

Yes, it includes specific tests for group closure, associativity, identity elements, and inverses to ensure your transformation set forms a valid mathematical group.

Does this skill provide code for testing?

Yes, it includes Python templates for testing invariance and equivariance, including sampling strategies for continuous and discrete groups.

Symmetry Validation Suite

Name: Symmetry Validation Suite
Author: lyndonkl

bylyndonkl

•

데이터 과학 및 ML

Validates symmetry hypotheses in datasets and models through empirical invariance and equivariance testing protocols.

The Symmetry Validation Suite helps machine learning engineers and researchers verify whether specific symmetries, such as rotation, translation, or permutation, actually exist in their data before implementing complex equivariant architectures. By providing concrete testing protocols, checking group structures, and analyzing distribution shifts, this skill ensures that model constraints are empirically grounded, preventing performance degradation caused by over-constraining or missing crucial symmetries. It offers a structured workflow from hypothesis generation to a final validation report, complete with recommended implementation strategies.

주요 기능

01Sampling strategies for continuous (SO2, SE3) and discrete (Cn, Sn) groups

02Data distribution analysis to identify 'approximate symmetry' ranges and breaking factors

03Standardized validation reporting with clear architectural recommendations

04Empirical invariance and equivariance testing with statistical significance metrics

058 GitHub stars

06Mathematical group structure verification including closure and associativity tests

사용 사례

01Confirming if a physics-informed model should enforce SO(3) rotational symmetry based on training data

02Determining whether to use hard equivariant constraints versus soft regularization for a new dataset

03Debugging model failures by checking if data augmentation covers necessary transformation invariants

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lyndonkl/claude symmetry-validation-suite

For use in Claude.ai and ChatGPT

Download Skill