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SimPy Discrete-Event Simulation

Build, test and analyze bounded queue and process simulations with SimPy 4.1.2

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
15 files

What it does

Once installed, the agent can design and build discrete-event simulations (models of queues, service lines, logistics or production systems over time) using the SimPy Python library. It follows guidance on bounding runs so they cannot loop forever, running independent replications with reproducible random seeds, and reporting limitations. It also ships small command-line tools that run bounded queue scenarios from local JSON settings and summarize trace and monitor files.

Great for

  • Engineers or analysts modelling queues, staffing, or throughput in Python
  • Students learning event-driven simulation and reproducible output analysis
  • Teams who want simulation studies with explicit run limits and documented assumptions

Skip it if

  • Causal or scientific conclusions from a simulation alone: SimPy does not validate the model, its inputs or its assumptions, and the skill says results must be checked against real system evidence
  • Non-Python environments or users unable to install Python 3.10+, uv and SimPy 4.1.2, since the bundled tools will not run without them
  • Very large or unbounded models: the bundled tools cap time, events, entities and replications, so larger studies need changes to those limits

Try asking

  • “Build a SimPy model of a two-server checkout queue and report waiting times with a warm-up period”
  • “Run 30 independent replications of my call-centre model and give me confidence intervals”
  • “Summarize this event trace CSV from my ResourceMonitor output”
  • “Check whether my SimPy process loop can stall at the same simulation time and add a guard”
Step-by-step guide: how to use SimPy Discrete-Event SimulationInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 15 original files, the MIT licence and an attribution note.

    simpy.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip simpy.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip simpy.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Ask the agent to state the question, time units, warm-up and run length before writing code, so the model matches the decision it supports.
  • Keep the bundled scripts' limits in mind; if a study needs larger horizons or entity counts, ask for a deliberate change rather than raising limits silently.
  • Review the generated Python and the scripts before running them, since the agent will execute them locally.

What's inside

  • references/cli-guide.md
  • references/events.md
  • references/monitoring.md
  • references/process-interaction.md
  • references/real-time.md
  • references/resources.md
  • references/simulation-methodology.md
  • references/sources.md
  • scripts/_common.py
  • scripts/basic_simulation_template.py
  • scripts/bounded_queue_scenario.py
  • scripts/event_trace_summary.py
  • scripts/replication_runner.py
  • scripts/resource_monitor.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.