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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/monte-carlo-simulation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How monte-carlo-simulation Compares
| Feature / Agent | monte-carlo-simulation | 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?
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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