What is SimPy used for?

SimPy is a Python framework used for discrete-event simulation, allowing you to model systems where entities interact with shared resources over time, such as factories, call centers, or computer networks.

Can SimPy handle real-time simulations?

Yes, SimPy includes a RealtimeEnvironment that can synchronize simulation events with wall-clock time, which is useful for hardware-in-the-loop testing or interactive applications.

How does this skill help with Claude Code?

This skill provides Claude with the specific patterns, best practices, and implementation logic needed to correctly use SimPy's generator-based process model and specialized resource managers.

Is SimPy suitable for continuous simulations?

SimPy is primarily designed for discrete events. For continuous simulations involving fixed time steps or differential equations, tools like SciPy ODE solvers are usually more appropriate.

Does it support resource prioritization?

Yes, the skill covers patterns for PriorityResource and PreemptiveResource to handle complex queuing scenarios where specific entities must take precedence over others.

SimPy Discrete-Event Simulation

Name: SimPy Discrete-Event Simulation
Author: x-cmd

byx-cmd

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データサイエンスとML

Builds and manages process-based discrete-event simulations in Python to model complex systems like manufacturing, logistics, and network traffic.

概要

SimPy is a powerful framework that allows developers to model real-world systems where entities compete for shared resources over time. This skill enables Claude to architect simulation environments, define process-based generator functions, manage resource contention, and implement sophisticated monitoring for performance analysis. It is particularly useful for optimizing workflows, capacity planning, and validating system behaviors in domains such as healthcare, telecommunications, and supply chain management before physical implementation.

主な機能

Advanced shared resource management including servers, containers, and stores
Process modeling using Python generator functions and yield statements
Real-time simulation capabilities for wall-clock synchronization
Comprehensive data collection and resource monitoring utilities
Event-driven scheduling and process synchronization patterns
8 GitHub stars

ユースケース

Analyzing waiting lines and service capacity for healthcare or retail operations
Optimizing manufacturing production lines and inventory management systems
Simulating network traffic, packet routing, and bandwidth allocation

概要

主な機能

Advanced shared resource management including servers, containers, and stores
Process modeling using Python generator functions and yield statements
Real-time simulation capabilities for wall-clock synchronization
Comprehensive data collection and resource monitoring utilities
Event-driven scheduling and process synchronization patterns
8 GitHub stars

ユースケース

Analyzing waiting lines and service capacity for healthcare or retail operations
Optimizing manufacturing production lines and inventory management systems
Simulating network traffic, packet routing, and bandwidth allocation