How is this different from ML-based color generation?

Unlike ML models which can be non-deterministic and act as a 'black box,' this skill uses cryptographic hashes to ensure colors are 100% reproducible, verifiable, and require zero external dependencies.

Can this be used for UI design systems?

Yes, it is ideal for automatically assigning unique, stable colors to UI components, features, or user roles based on their unique identifiers without manually managing a color registry.

What is frame-invariance in this context?

Frame-invariance ensures that the color generated remains identical regardless of the order of data or the perspective of the observer, satisfying the mathematical involution property (ι∘ι = id).

Does this skill require Julia to run?

While the core logic is demonstrated in Julia, the algorithm is language-agnostic and can be implemented in Python, JavaScript, or any environment with a standard hashing function.

Deterministic Color Generator

Name: Deterministic Color Generator
Author: plurigrid

byplurigrid

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Design Tools

Generates consistent, reproducible hex colors from metadata using cryptographic hashing instead of machine learning models.

This skill implements a transparent color generation system that maps metadata strings—such as skill, project, or world names—directly to a 24-bit RGB color space. By utilizing deterministic hashing instead of black-box ML models, it ensures 100% reproducibility across different platforms and tools without external dependencies. It is particularly useful for complex data visualizations like IsUMAP, where visual markers must remain stable across different perspectives and topology updates, maintaining mathematical properties like frame-invariance and involution symmetry.

Key Features

01Zero-dependency implementation using standard hashing functions

02Frame-invariant design ensuring consistency across observer perspectives

03100% deterministic color mapping from metadata strings

04Support for 4-level hierarchical color structures (World, Phase, Project, Skill)

05High-contrast color distribution with negligible collision probability

062 GitHub stars

Use Cases

01Maintaining consistent color coding for skills across interactive HTML and JSON exports

02Visualizing large-scale topological clusters where visual stability is critical

03Generating design system palettes on-the-fly without external asset tables

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi deterministic-color-generation

For use in Claude.ai and ChatGPT

Key Features

01Zero-dependency implementation using standard hashing functions

02Frame-invariant design ensuring consistency across observer perspectives

03100% deterministic color mapping from metadata strings

04Support for 4-level hierarchical color structures (World, Phase, Project, Skill)

05High-contrast color distribution with negligible collision probability

062 GitHub stars

Use Cases

01Maintaining consistent color coding for skills across interactive HTML and JSON exports

02Visualizing large-scale topological clusters where visual stability is critical

03Generating design system palettes on-the-fly without external asset tables

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi deterministic-color-generation

For use in Claude.ai and ChatGPT