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SimPy Discrete-Event Simulation

Build, test, and analyze bounded process-based discrete-event simulations in SimPy with correct event semantics and replication-based output analysis.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Gives a full workflow for process-based discrete-event models on pinned SimPy 4.1.2: purpose/estimands → conceptual model → generators → bounded runs → verification → replications → limitations.
  • Documents core semantics precisely: event lifecycle, env.run(until=...) boundary differences, condition events (AnyOf/AllOf), interrupts, and preemption via the Preempted cause.
  • Covers every shared-resource type (Resource, PriorityResource, PreemptiveResource, Container, Store, FilterStore, PriorityStore) and their queue rules.
  • Enforces bounded execution (time, entity, event, replication caps) and time-weighted, non-intrusive monitoring.
  • Ships safe CLIs: built-in bounded queue scenario, replication runner with Student-t intervals, event trace summarizer, and config validator — no network, no eval of user code.

Who it's for

  • Operations researchers and engineers modeling queues, production lines, logistics/inventory, or network traffic.
  • Python users who need reproducible simulation runs with defensible confidence intervals.
  • Anyone who wants deterministic verification tests instead of ad-hoc simulation scripts.

Examples

  • "Simulate a 2-server desk with 4-min mean interarrivals and 6-min service over 480 minutes" → produces a bounded model and reports unfinished entities instead of hiding them.
  • "Give me a 95% CI for mean wait over 30 independent replications" → uses replication_runner.py; single-replication intervals are refused.
  • "Make a preempted job resume its remaining work" → applies the Interrupt/Preempted handling pattern with correct request cancellation.

· · · Install guide · · ·

Try it now, no install

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Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/simpy/SKILL.md

What I want: (describe your task here)

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↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
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Install in Claude Code

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Install the skill I found on AI Skill Mart.
Copy the skills/simpy folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/simpy/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/simpy ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal.
  2. Clone the repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy just this skill: cp -r scientific-agent-skills/skills/simpy ~/.claude/skills/
  5. Set up the pinned environment: uv venv --python 3.13 && source .venv/bin/activate && uv pip install "simpy==4.1.2"
  6. Restart Claude Code and ask something like "Model an M/M/2 queue in SimPy with a 480-minute horizon".
  7. Verify the CLIs: python ~/.claude/skills/simpy/scripts/bounded_queue_scenario.py --help
View source on GitHub ↗License: MIT