What is SimPy used for in Claude Code?

SimPy is used within Claude Code to help you architect and implement discrete-event simulations for systems where processes interact with limited resources, such as logistics or manufacturing.

Does the SimPy skill help with data collection?

Yes, it includes reusable monitoring scripts and templates for tracking resource utilization, wait times, and exporting results to CSV for further analysis.

Is SimPy suitable for continuous physics simulations?

No, SimPy is specialized for discrete events occurring at specific points in time. For continuous mathematical modeling, tools like SciPy ODE solvers are recommended.

Can this skill handle real-time simulations?

Yes, this skill includes support for SimPy's RealtimeEnvironment, which allows you to synchronize simulation time with actual wall-clock time for hardware-in-the-loop testing.

How does the skill handle resource contention?

It provides patterns for using various resource types, including standard Resources, PriorityResources for tiered queuing, and PreemptiveResources for high-priority task interruption.

SimPy Simulation

Name: SimPy Simulation
Author: lifangda

bylifangda

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データサイエンスとML

Builds and optimizes discrete-event simulation models to analyze complex systems like logistics, manufacturing, and networks.

概要

The SimPy skill empowers users to create process-based discrete-event simulations using standard Python generator functions. It is specifically designed for modeling systems where entities such as customers, vehicles, or data packets interact with shared resources like servers, machines, or bandwidth over time. By leveraging this skill, developers can perform capacity planning, analyze queue dynamics, and optimize workflows in environments where events occur at irregular intervals, providing deep insights into system behavior before physical implementation.

主な機能

Real-time simulation capabilities synchronized with wall-clock time
Process modeling using Python generator functions and yield statements
16 GitHub stars
Advanced resource management including priority and preemptive queuing
Event-driven scheduling for irregular interval system simulations
Integrated monitoring templates for data collection and bottleneck analysis

ユースケース

Simulating network traffic, packet routing, and bandwidth allocation
Optimizing manufacturing production lines and warehouse inventory management
Analyzing service operations like hospital emergency rooms or retail checkout flow

概要

主な機能

Real-time simulation capabilities synchronized with wall-clock time
Process modeling using Python generator functions and yield statements
16 GitHub stars
Advanced resource management including priority and preemptive queuing
Event-driven scheduling for irregular interval system simulations
Integrated monitoring templates for data collection and bottleneck analysis

ユースケース

Simulating network traffic, packet routing, and bandwidth allocation
Optimizing manufacturing production lines and warehouse inventory management
Analyzing service operations like hospital emergency rooms or retail checkout flow