Julia Gay Skill

Trit: +1 (PLUS - generative color computation)
Foundation: Gay.jl + SplitMix64 + SPI

Core Concept

Gay.jl provides:

• Deterministic color from seed + index
• GF(3) trit classification
• SPI-compliant parallel fingerprints
• Wide-gamut color space support

API

using Gay

# Color at index
color = color_at(seed, index)
# => (r=0.65, g=0.32, b=0.88)

# Palette generation
palette = Gay.palette(seed, 5)

# Trit classification
trit = Gay.trit(color)  # => -1, 0, or +1

# XOR fingerprint
fp = Gay.fingerprint(colors)

SPI Guarantees

# Strong Parallelism Invariance
@assert fingerprint(colors_thread1) ⊻ fingerprint(colors_thread2) == 
        fingerprint(vcat(colors_thread1, colors_thread2))

Ergodic Bridge

using Gay: ErgodicBridge

# Create time-color bridge
bridge = create_bridge(seed, n_colors)

# Verify bidirectionally
verify_bridge(bridge)

# Detect obstructions
obstructions = detect_obstructions(seed, n_samples)

Canonical Triads

bisimulation-game (-1) ⊗ acsets (0) ⊗ julia-gay (+1) = 0 ✓
sheaf-cohomology (-1) ⊗ bumpus-narratives (0) ⊗ julia-gay (+1) = 0 ✓
spi-parallel-verify (-1) ⊗ triad-interleave (0) ⊗ julia-gay (+1) = 0 ✓

Julia Scientific Package Integration

From julia-scientific skill - related Julia packages for color/visualization:

Bridge to Scientific Domains

# Molecular visualization with deterministic colors
using Gay, MolecularGraph, CairoMakie

mol = smilestomol("CCO")
atom_colors = [Gay.color_at(seed, i) for i in 1:natoms(mol)]
visualize_molecule(mol, colors=atom_colors)

# Single-cell UMAP with Gay.jl cluster colors
using Gay, SingleCellProjections, CairoMakie

clusters = cluster(adata)
cluster_colors = Gay.palette(seed, n_clusters)
scatter(umap_coords, color=cluster_colors[cluster_labels])

See Also

• gay-mcp - MCP server for color generation
• triad-interleave - 3-stream scheduling
• world-hopping - Badiou possible world navigation
• julia-scientific - Full Julia package mapping (137 skills)

Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

Visualization

• matplotlib [○] via bicomodule
  • Hub for all visualization

Bibliography References

• general: 734 citations in bib.duckdb

Cat# Integration

This skill maps to Cat# = Comod(P) as a bicomodule in the equipment structure:

Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826

GF(3) Naturality

The skill participates in triads satisfying:

(-1) + (0) + (+1) ≡ 0 (mod 3)

This ensures compositional coherence in the Cat# equipment structure.