Agent skill · research science · k-dense-ai
simpy
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.
Why this skill is useful
Provides domain-specific knowledge and patterns for building discrete-event simulations using SimPy that the AI wouldn't reliably generate on its own.
What it needs
Requires python, uv installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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: 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 1. Define purpose and estimands. State the decision/question, system boundary, entities, resources, state, outputs, time units, and terminating event or steady-state target. 2. Write a conceptual model first. Record assumptions, distributions, routing, priorities, initial conditions, and omitted mechanisms. 3. Implement generators. A SimPy process is an event-yielding Python generator. Register the generator object with env.process(...). 4. Bound execution. Give every production run explicit time, entity, event, and replication caps. Never call env.run() on a model containing an endless process. 5. Separate random streams. Use local RNG instances for logically distinct stochastic sources; retain a seed manifest. 6. Instrument deliberately. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/simpy