monte-carlo-physics-simulator
Monte Carlo simulation skill for statistical physics, particle transport, and stochastic processes
Profile →npx skills add a5c-ai/babysitter --skill monte-carlo-physics-simulator --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Monte Carlo Physics Simulator Skill ## Purpose Provide Monte Carlo simulation capabilities for statistical physics, particle transport, and stochastic processes in physics applications. ## Capabilities - Metropolis algorithm implementation - Wang-Landau sampling - Parallel tempering coordination - Variance reduction techniques - Autocorrelation analysis - Error estimation and jackknife/bootstrap ## Usage Guidelines - Choose appropriate sampling algorithms for the problem - Implement variance reduction for rare events - Monitor autocorrelation for independent samples - Use proper error estimation techniques ## Dependencies - Custom MC codes - OpenMC - Geant4 ## Process Integration - Monte Carlo Simulation Implementation - Statistical Analysis Pipeline - Monte Carlo Event Generation
- Purpose
- Capabilities
- Usage Guidelines
- Dependencies
- Process Integration
What does the monte-carlo-physics-simulator skill do?
Monte Carlo simulation skill for statistical physics, particle transport, and stochastic processes
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill monte-carlo-physics-simulator --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.