What is bijective index recovery?

It is a property that allows you to work backwards: if you have a color and the initial seed, you can mathematically determine the exact index 'n' that generated that color.

Can this skill handle high-dimensional color spaces?

Yes, sequences like Sobol and Halton are specifically designed to maintain excellent uniformity in high-dimensional spaces, extending far beyond simple 1D hue rotations.

What are low-discrepancy sequences used for?

Low-discrepancy sequences (LDS) are used to create uniform distributions that cover a space more evenly than random sampling. This is ideal for generating color palettes where you want maximum distinction between items.

Which mathematical sequences are included?

The skill includes the Golden Angle, Plastic Constant, Halton, R-sequence, Kronecker, Sobol, Pisot sequences, and Continued Fractions.

Low-Discrepancy Color Sequences

Name: Low-Discrepancy Color Sequences
Author: plurigrid

byplurigrid

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디자인 도구

Generates deterministic, uniformly distributed color spaces and patterns using advanced low-discrepancy mathematical sequences.

This skill empowers Claude to utilize sophisticated mathematical sequences—such as the Golden Angle, Plastic Constant, and Sobol sequences—to generate optimally distributed color sets. Unlike random generation, these low-discrepancy sequences ensure maximum coverage and minimal clustering within color dimensions (hue, saturation, or RGB space). A standout feature is the bijective index property, which allows developers to recover the exact sequence index from a generated color and seed, enabling powerful temporal reconstruction and reafference capabilities in generative design and data visualization.

주요 기능

012 GitHub stars

02Geodesic path mapping through continued fraction expansions for hyperbolic geometry

03Bijective index recovery to map specific colors back to their original sequence positions

04Multi-dimensional sequence support including Golden Angle (1D) and Plastic Constant (2D)

05High-dimensional distribution support via Sobol, Halton, and R-sequences

06Seamless integration with the gay-mcp ecosystem for deterministic color generation

사용 사례

01Assigning unique, reproducible visual identifiers to system components or nodes

02Generating distinct, non-overlapping color palettes for complex data visualizations

03Implementing quasi-Monte Carlo sampling for uniform spatial distribution in UI/UX design

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi low-discrepancy-sequences

For use in Claude.ai and ChatGPT

Download Skill