Best use case
gay-monte-carlo is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Gay Monte Carlo Measurements
Teams using gay-monte-carlo should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/gay-monte-carlo/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How gay-monte-carlo Compares
| Feature / Agent | gay-monte-carlo | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Gay Monte Carlo Measurements
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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