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simpy skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Build, inspect, test, and analyze bounded process-based discrete-event simulations with SimPy, including events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis.

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Install the simpy skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/simpy ~/.claude/skills/simpy
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

SimPy

Scope

Use this skill for process-based discrete-event models where active entities yield events and contend for resources: queues, production systems, logistics, networks, service operations, inventory, and other event-driven systems.

SimPy supplies an event scheduler and modeling primitives. It does not choose a scientifically valid conceptual model, input distribution, warm-up, run length, replication count, estimand, or causal interpretation. Treat those as simulation-study methodology, not SimPy API behavior.

Current release and installation

Verified 2026-07-23:

4.1.2 points to commit f4381649.

  • Latest stable: SimPy 4.1.2, released on PyPI 2026-05-24; source tag

plus PyPy. SimPy has no runtime dependencies.

  • Package metadata requires Python >=3.8 and classifies CPython 3.8-3.14
  • 4.1.2 adds Python 3.13/3.14 support and modern-interpreter test fixes.
  • Upstream and this skill are MIT-licensed.

Create a reproducible environment:

uv venv --python 3.13
source .venv/bin/activate
uv pip install "simpy==4.1.2"
python -c "import importlib.metadata; print(importlib.metadata.version('simpy'))"

Do not silently substitute the latest documentation build: it may describe an unreleased development revision. Use the versioned 4.1.2 links in references/sources.md.

Model workflow

entities, resources, state, outputs, time units, and terminating event or steady-state target.

  1. Define purpose and estimands. State the decision/question, system boundary,

routing, priorities, initial conditions, and omitted mechanisms.

  1. Write a conceptual model first. Record assumptions, distributions,

Register the generator object with env.process(...).

  1. Implement generators. A SimPy process is an event-yielding Python generator.

replication caps. Never call env.run() on a model containing an endless process.

  1. Bound execution. Give every production run explicit time, entity, event, and

stochastic sources; retain a seed manifest.

  1. Separate random streams. Use local RNG instances for logically distinct

close time-weighted intervals at the horizon, and test that monitoring does not alter event order.

  1. Instrument deliberately. Observe state after the transition of interest,

traces, queue discipline, and analytical benchmarks; compare against system or expert evidence for the stated purpose.

  1. Verify and validate. Test deterministic edge cases, conservation identities,

estimates, not correlated entities within one run.

  1. Run independent replications. Make intervals from replication-level

seeds/streams, precision, sensitivity, and validation evidence. Never convert simulation association into a causal claim.

  1. Report limitations. Include initialization, unfinished entities, run length,

Read references/simulation-methodology.md before making inferential claims.

Minimal bounded model

import random
import simpy

HORIZON = 480.0
arrival_rng = random.Random(101)
service_rng = random.Random(202)
env = simpy.Environment()
server = simpy.Resource(env, capacity=2)
completed = []

def customer(arrival):
    with server.request() as request:
        yield request
        wait = env.now - arrival
        yield env.timeout(service_rng.expovariate(1 / 6.0))
    completed.append((env.now, wait))

def arrivals():
    for _ in range(10_000):  # Entity cap.
        delay = arrival_rng.expovariate(1 / 4.0)
        if env.now + delay >= HORIZON:
            return
        yield env.timeout(delay)
        env.process(customer(env.now))

env.process(arrivals())
env.run(until=HORIZON)

The numeric horizon is half-open: normal events scheduled exactly at 480.0 are not processed. Report unfinished entities rather than silently treating them as completed observations.

Core semantics

Environment and deterministic ordering

Environment is single-threaded. The queue is ordered by simulation time, event priority, then a strictly increasing event ID. Same-time, same-priority events are therefore processed FIFO in scheduling order. Model processes may represent concurrency, but callbacks execute sequentially and deterministically.

  • env.now: unitless simulation clock; choose and document one unit.
  • env.peek(): next event time or infinity.
  • env.step(): process one event; raises EmptySchedule when empty.
  • env.active_process: currently executing process, otherwise None.
  • env.run(): drain the queue; unsafe with recurring or endless processes.

env.run(until=number) and env.run(until=event) are not interchangeable at boundaries:

that exact time.

  • A numeric value schedules an urgent stop event and excludes ordinary events at

Other same-time ordering depends on priority and scheduling order.

  • An Event criterion returns that event's value when its stop callback fires.

StopSimulation by rescheduling the target. Consequently, after env.run(until=target), target.processed can remain False until one more step()/run() even though its value was returned. Do not use processed as the sole post-run completion test.

  • In 4.1.2, Environment.step() preserves callbacks remaining after

See references/events.md and references/monitoring.md.

Event, Timeout, Process, and Condition

succeed(value) or fail(exception) triggers it once.

  • An Event moves once through not-triggered -> triggered/scheduled -> processed.

manually succeeded again.

  • A Timeout triggers when created, is scheduled for now + delay, and cannot be

yielded event value. Returning from the generator succeeds the Process with that return value. Uncaught exceptions fail it.

  • env.process(generator) creates a Process; the generator resumes with the

dict-like mapping from event objects to their values. Test membership using the original event objects; do not assume a scalar result.

  • AnyOf / a | b and AllOf / a & b yield a ConditionValue: an ordered,

requests when abandoning them; ordinary timeouts remain scheduled.

  • AnyOf does not cancel losing events. Explicitly cancel pending resource

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