monte-carlo-simulation

Pass

Monte Carlo methods for uncertainty quantification

@a5c-aiMIT2/22/2026

(0)

242stars

1downloads

3views

Install Skill

Skills are third-party code from public GitHub repositories. SkillHub scans for known malicious patterns but cannot guarantee safety. Review the source code before installing.

Install with CLI

Install globally (user-level):

npx skillhub install a5c-ai/babysitter/monte-carlo-simulation

Install in current project:

npx skillhub install a5c-ai/babysitter/monte-carlo-simulation --project

Suggested path: ~/.claude/skills/monte-carlo-simulation/

SKILL.md Content

---
name: monte-carlo-simulation
description: Monte Carlo methods for uncertainty quantification
allowed-tools:
  - Bash
  - Read
  - Write
  - Edit
  - Glob
  - Grep
metadata:
  specialization: mathematics
  domain: science
  category: uncertainty-quantification
  phase: 6
---

# Monte Carlo Simulation

## Purpose

Provides Monte Carlo methods for uncertainty quantification, integration, and probabilistic analysis.

## Capabilities

- Standard Monte Carlo sampling
- Importance sampling
- Stratified sampling
- Quasi-Monte Carlo (Sobol, Halton sequences)
- Markov chain Monte Carlo
- Convergence analysis

## Usage Guidelines

1. **Sampling Strategy**: Choose appropriate sampling method
2. **Sample Size**: Determine sufficient sample sizes
3. **Variance Reduction**: Apply variance reduction techniques
4. **Convergence**: Monitor convergence diagnostics

## Tools/Libraries

- NumPy
- scipy.stats
- SALib