monte-carlo-simulation

Monte Carlo methods for uncertainty quantification

509 stars

Best use case

monte-carlo-simulation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Monte Carlo methods for uncertainty quantification

Teams using monte-carlo-simulation 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

$curl -o ~/.claude/skills/monte-carlo-simulation/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/science/mathematics/skills/monte-carlo-simulation/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/monte-carlo-simulation/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How monte-carlo-simulation Compares

Feature / Agentmonte-carlo-simulationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Monte Carlo methods for uncertainty quantification

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

# 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

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