Skip to content
Back to skills

Gay Monte Carlo

ASecurity

Gay Monte Carlo Measurements

  • 666 stars
  • 0 votes
  • 0 copies
  • 1 view
  • Added September 2, 2026
ai-agentsgo

Security analysis

A100/100

Scanned September 2, 2026

npx -y skills add majiayu000/claude-skill-registry --skill gay-monte-carlo --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Gay Monte Carlo?

Add the live security badge to your README. It updates with every re-scan.

Security grade badge for Gay Monte Carlo
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/majiayu000-gay-monte-carlo/badge)](https://www.skillsdirectory.com/skills/majiayu000-gay-monte-carlo)

More formats (shields.io, HTML) on the badges page. Keep it an A: scan every change in CI with Pro.

Download with Pro
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

Attribution

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments

Loading comments…

Newsletter

Get the weekly list of safe new skills, and the ones we pulled.