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gay-monte-carlo
Gay Monte Carlo Measurements
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This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Stars
9
Hot score
84
Updated
March 20, 2026
Overall rating
C3.1
Composite score
3.1
Best-practice grade
B78.7
Install command
npx @skill-hub/cli install plurigrid-asi-gay-monte-carlo
Repository
Best for
Primary workflow: Ship Full Stack.
Technical facets: Full Stack.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: plurigrid.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install gay-monte-carlo into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/plurigrid/asi before adding gay-monte-carlo to shared team environments
- Use gay-monte-carlo for development workflows
Works across
Claude CodeCodex CLIGemini CLIOpenCode
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: gay-monte-carlo
description: Gay Monte Carlo Measurements
version: 1.0.0
---
# Gay Monte Carlo Measurements
---
name: gay-monte-carlo
description: Monte Carlo uncertainty propagation with Gay.jl deterministic coloring and Enzyme.jl autodiff for gamut-aware probability distributions.
trit: 1
color: "#77DEB1"
---
## Overview
**GayMonteCarloMeasurements.jl** extends MonteCarloMeasurements.jl with Gay.jl chromatic identity for deterministic color-coded uncertainty propagation.
## Core Concepts
### Particles as Colored Distributions
```julia
using MonteCarloMeasurements
using Gay
# Construct uncertain parameters with color tracking
gay_seed!(0xcd0a0fde6e0a8820)
a = π ± 0.1 # Particles{Float64,2000}
# Propagate through nonlinear functions
sin(a) # → Particles with full distribution
```
### Enzyme Gamut Learning
```julia
using Enzyme
# Learnable colorspace parameters
params = OkhslParameters()
function loss(params, seed, target_gamut=:srgb_boundary)
color = forward_color(params, projection, seed)
gamut_penalty = out_of_gamut_distance(color, target_gamut)
bandwidth_reward = color_distinctiveness(color)
return gamut_penalty - 0.1 * bandwidth_reward
end
∂params = Enzyme.gradient(Reverse, loss, params, seed)
```
## Features
- **Nonlinear uncertainty propagation** - Handles x², sign(x), integration
- **Correlated quantities** - Multivariate particles
- **Distribution fitting** - `fit(Gamma, p)` for any Particles
- **Visualization** - `plot(p)` shows histogram, `density(p)` shows KDE
- **SPI verification** - Fingerprint matching across network
## GF(3) Integration
| Trit | Role | Operation |
|------|------|-----------|
| +1 | PLUS | Generative sampling |
| 0 | ERGODIC | Distribution transport |
| -1 | MINUS | Constraint verification |
## Self-Avoiding Walk
```
next_color() → visited check
│
├─ fresh → XOR into fingerprint
│
└─ collision → triadic fork
```
## Repository
- **Source**: bmorphism/GayMonteCarloMeasurements.jl
- **Seed**: `0xcd0a0fde6e0a8820`
- **Index**: 103/1055
## Related Skills
- `gay-julia` - Core Gay.jl integration
- `spi-parallel-verify` - Fingerprint verification
- `fokker-planck-analyzer` - Equilibrium analysis