A Python toolkit for discrete-event simulation, where processes advance when events occur and compete for limited resources. It can represent queues, production systems, logistics, networks, and service operations.

Use it to model queues, inventory, manufacturing, logistics, networks, and other systems involving events, resources, interruptions, monitoring, and repeated simulations.

It lets you test event-driven processes without waiting for the real system to unfold. You still need to choose a valid model, input distributions, warm-up period, and run length.

simpy — Skill for Claude Code by K-Dense-AI

About the project

Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.

K-Dense-AI/scientific-agent-skills · 44,003 stars · on GitHub · arxiv.org

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add K-Dense-AI/scientific-agent-skills --skill simpy

Clone the repo

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/simpy/github.svg)](https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/simpy)

Your own site

<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/simpy"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/simpy/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/simpy"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/simpy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,079 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

Socket pass 9 Apr 2026
Snyk pass 9 Apr 2026
NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00042	$0.03079
Opus 5	$0.00021	$0.01540
Sonnet 5	$0.00008	$0.00616
Haiku 4.5	$0.00004	$0.00308

Measured 6d ago against content hash eb485cdd848d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

simpy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/_common.py, scripts/basic_simulation_template.py, scripts/bounded_queue_scenario.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/simpy/SKILL.md · 301 lines

How it starts

The opening of the file, as written. The whole thing — 301 lines — stays where its author put it; the contents beside it link to each section 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:

Latest stable: SimPy 4.1.2, released on PyPI 2026-05-24; source tag 4.1.2 points to commit f4381649.
Package metadata requires Python >=3.8 and classifies CPython 3.8-3.14 plus PyPy. SimPy has no runtime dependencies.
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

Define purpose and estimands. State the decision/question, system boundary, entities, resources, state, outputs, time units, and terminating event or steady-state target.
Write a conceptual model first. Record assumptions, distributions, routing, priorities, initial conditions, and omitted mechanisms.
Implement generators. A SimPy process is an event-yielding Python generator. Register the generator object with env.process(...).
Bound execution. Give every production run explicit time, entity, event, and replication caps. Never call env.run() on a model containing an endless process.
Separate random streams. Use local RNG instances for logically distinct stochastic sources; retain a seed manifest.
Instrument deliberately. Observe state after the transition of interest, close time-weighted intervals at the horizon, and test that monitoring does not alter event order.
Verify and validate. Test deterministic edge cases, conservation identities, traces, queue discipline, and analytical benchmarks; compare against system or expert evidence for the stated purpose.
Run independent replications. Make intervals from replication-level estimates, not correlated entities within one run.
Report limitations. Include initialization, unfinished entities, run length, seeds/streams, precision, sensitivity, and validation evidence. Never convert simulation association into a causal claim.

Read the full file on GitHub · 301 lines

Files

What ships with it

15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

6d ago First seen · 301 lines · 42 tokens per session scan A eb485cdd848d

Subscribe to this mod's changes

simpy is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,003 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 3,079 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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