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SimPy Discrete-Event Simulation

An expert-level skill for building, testing, and analyzing bounded process-based discrete-event simulations with SimPy.

Data & AnalyticsAdvanced★ 33,030⑂ 3,248AI score 8/10Last updated: Aug 9, 2026

What it does

Guides the full lifecycle of a process-based (generator) discrete-event model on SimPy 4.1.2.

  • Core semantics: deterministic event ordering in Environment, the Event/Timeout/Process lifecycle, AnyOf/AllOf condition values, and correct interrupt() handling.
  • Resource modeling: a comparison table for Resource, PriorityResource, PreemptiveResource, Container, Store, FilterStore, and PriorityStore, including Preempted cause fields.
  • Bounded execution by default: mandatory caps on time, entities, events, and replications; explains the half-open boundary of env.run(until=number) and how to report unfinished entities.
  • Measurement and statistics: time-weighted averages (area under the state path), monitor non-interference tests, and replication-level Student-t confidence intervals.
  • Bundled safe CLIs: a built-in queue scenario, replication runner, event-trace summarizer, and config validator — no network calls, no execution of user-supplied Python.

Who it's for

  • Industrial engineers and operations analysts sizing queues and throughput for call centers, clinics, factories, or warehouses.
  • Data analysts comparing inventory policies, service levels, or server capacity via simulation.
  • Researchers who must defend simulation results methodologically: warm-up, replication count, seed manifests, and stated limitations.
  • Python developers who keep hitting bugs from misunderstanding SimPy's event ordering or preemption rules.

Examples

  1. Teller staffing study: with 15-minute mean interarrivals and 6-minute mean service, run 30 replications for 2 vs. 3 servers, compare 95% intervals on mean wait, and get a warm-up recommendation to remove initialization bias.
  2. Machine failure modeling: inject breakdowns via process.interrupt(), preserve remaining work on resume, and evaluate output across preventive-maintenance intervals with deterministic verification tests.
  3. Fixing instrumentation: convert an event-sample average of queue length into a proper time-weighted average, then add unit tests proving the monitor does not alter event order.

· · · Install guide · · ·

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.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude 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 and cd into your working directory.
  2. Clone the repository: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  3. Create the skills folder: mkdir -p ~/.claude/skills
  4. Copy the skill: cp -r scientific-agent-skills/skills/simpy ~/.claude/skills/
  5. Set up the pinned environment (Python 3.13 recommended, uv required): uv venv --python 3.13 && source .venv/bin/activate && uv pip install "simpy==4.1.2"
  6. Verify: python -c "import simpy; print(simpy.__version__)"
  7. Restart Claude Code and try a prompt such as "Build a bounded SimPy queue model with 2 servers and report mean wait."
  8. To use the bundled CLIs, inspect options first: python ~/.claude/skills/simpy/scripts/bounded_queue_scenario.py --help.
View source on GitHub ↗License: MIT