gay-julia

Generate deterministic wide-gamut LCH colors from seeds using SplitMix64 and GF(3) trits in Julia/Jupyter notebooks via Gay.jl.

60|13|Updated Dec 22, 2025
One-click install
npx skills add https://github.com/plurigrid/asi --skill gay-julia
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gay-julia
Source: https://github.com/plurigrid/asi/tree/main/skills/gay-julia
Command: npx skills add https://github.com/plurigrid/asi --skill gay-julia

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Deterministic color sampling using Gay.jl enables repeatable color generation for visualizations and UI THEMING across tools and platforms.

Core Features & Use Cases

  • Deterministic color generation from a seed
  • GF(3) trit classification for balanced color streams
  • SPI-compliant fingerprints for parallel workflows and reproducibility

Quick Start

In Julia, import Gay and call color = gay_color(seed) or palette(seed, n) to generate colors.

Frequently Asked Questions about gay-julia

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate deterministic colors from a seed in Julia?

Deterministic color generation in Julia uses Gay.jl to produce repeatable colors from a seed value. Call gay_color(seed) for a single color or palette(seed, n) to generate n colors. This ensures consistent results across runs and platforms for UI theming, visualizations, and parallel workflows.

What is GF(3) trit classification and why does it matter for color sampling?

GF(3) trit classification balances color streams by organizing samples into three states. The gf3_trit(seed) function applies this algebraic structure to ensure color distributions remain evenly spread, improving visual harmony in deterministic palettes used across conflict resolution and terminal coloring.

Can I use deterministic color generation for parallel task coloring?

Yes. Deterministic color generation with SPI-compliant fingerprints enables reproducible coloring across parallel workflows. SplitMix64-based seeding ensures that the same task identifier always maps to the same color, maintaining consistency in distributed systems and CRDT conflict resolution.

How do I apply LCH colors and LispSyntax integration to my color workflow?

Gay.jl exposes the colorize(sexp, seed) function to apply deterministic LCH colors to Lisp S-expressions. This integrates wide-gamut LCH color space with LispSyntax, enabling semantic color assignment for structured data in terminal sessions and programmatic UIs.

What makes wide-gamut LCH color sampling better than standard RGB for deterministic theming?

Wide-gamut LCH sampling preserves perceptual uniformity and color distinctness across larger color spaces than RGB. LCH's perceptual axes (lightness, chroma, hue) ensure deterministically generated palettes remain visually balanced and accessible for UI theming across diverse displays and platforms.