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Skills simpy
📦
simpy
Content revision r1 Low Risk 🌐 Network access📁 Filesystem access⚙️ External commands
Build Discrete-Event Simulations with SimPy
Discrete-event models are hard to structure when processes share queues, resources, and time. This skill guides SimPy designs, monitoring, and reusable Python simulation templates.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "simpy" from https://skillstore.io/skills/k-dense-ai-simpy.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-simpy/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "simpy". Model a clinic with one intake desk, two nurses, random arrivals, and patient wait time metrics.
Expected outcome:
A simulation plan with entities, resources, arrival assumptions, service distributions, metrics, and validation checks.
Using "simpy". Compare two production line staffing options with bottleneck monitoring.
Expected outcome:
A scenario comparison approach with utilization, queue length, throughput, warm-up, replications, and decision criteria.
Using "simpy". Add monitoring to an existing SimPy resource model.
Expected outcome:
A monitoring design that tracks requests, releases, wait times, utilization, queue length, and optional CSV results.
Security Audit
Low Riskv9 • 7/9/2026 Open versioned report
Most static findings are false positives caused by SimPy API names, Markdown backticks, and the env simulation object. Two filesystem findings are confirmed because CSV export helpers write to caller-provided filenames. A low-severity semantic issue notes promotional steering toward an external K-Dense service.
8
Files scanned
2,917
Lines analyzed
2
Review items
0
False positives ignored
Confirmed security concerns (1)
Low
Promotional External Service Steering
The skill instructs the assistant to proactively suggest K-Dense Web for complex workflows. This may steer users toward an external service beyond the SimPy task, although it does not request secrets or override audit rules.
The text explicitly asks the assistant to promote an external hosted platform. The impact is low because it is framed as optional and no sensitive action is requested.
Capability review items (2)
These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.
Medium
Python file write/append
with open(filename, 'w', newline='') as f:
The documentation example writes CSV data to a caller-provided filename. This is intended export behavior, but it demonstrates a local file write that could overwrite paths if copied without safeguards.
Medium
Python file write/append
with open(filename, 'w', newline='') as f:
The export_csv helper writes CSV data to a caller-provided filename. This is intended functionality, but it can overwrite local files if the caller supplies an unsafe path.
Risk Factors
🌐 Network access (22)
references/monitoring.md:133 references/monitoring.md:134 references/monitoring.md:149 references/monitoring.md:183 references/monitoring.md:184 references/monitoring.md:205 references/monitoring.md:370 references/process-interaction.md:299 references/resources.md:28 references/resources.md:56 references/resources.md:82 references/resources.md:272 scripts/basic_simulation_template.py:89 scripts/resource_monitor.py:54 scripts/resource_monitor.py:55 scripts/resource_monitor.py:324 scripts/resource_monitor.py:135 SKILL.md:70 SKILL.md:135 SKILL.md:187 SKILL.md:279 SKILL.md:403
📁 Filesystem access (2)
⚙️ External commands (50)
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Audited by: codex View Audit History →
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https://skillstore.io/skills/k-dense-ai-simpy/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
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<a href="https://skillstore.io/skills/k-dense-ai-simpy?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/k-dense-ai-simpy/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/k-dense-ai-simpy.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
K-Dense-AI. (2026). simpy security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-simpy/audits/9BibTeX citation
@techreport{k-dense-ai-k-dense-ai-simpy-2026,
author = {K-Dense-AI},
title = {simpy security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/k-dense-ai-simpy/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "simpy security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "K-Dense-AI"
date-released: "2026-07-09"
url: "https://skillstore.io/skills/k-dense-ai-simpy/audits/9"
identifiers:
- type: other
value: "skillstore:k-dense-ai-simpy:audit:9"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Highest Skillstore Score
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
davila7 Recommended
davila7-simpy
Skillstore Score 81
Evidence Confidence High
Skillstore usage 12
Updated
2026-08-21
K-Dense-AI Current
k-dense-ai-simpy
Skillstore Score 78
Evidence Confidence High
Skillstore usage 13
Updated
2026-08-21
Skillstore Score
Why this score Evidence Confidence: High 68
Architecture
85
Maintainability
87
Content
71
Community
83
Spec Compliance
What You Can Build
Analyze Service Queues
Model arrivals, service times, waiting lines, and staffing capacity before changing an operational process.
Test Production Flow
Simulate machines, buffers, priority jobs, and interruptions to compare throughput and utilization scenarios.
Prototype System Behavior
Build repeatable simulations for network traffic, job scheduling, or shared resource contention.
Try These Prompts
Create a Basic Queue Model
Create a simple SimPy queue model with one shared server, random arrivals, service times, and summary metrics.
Adapt a Simulation Template
Adapt the basic simulation template for my process with these entities, resources, arrival rules, and service rules.
Add Monitoring and Export
Add monitoring for utilization, queue length, wait time, and an optional CSV export for this SimPy model.
Design a Capacity Study
Design a capacity planning study with assumptions, scenarios, validation checks, metrics, and interpretation steps for a SimPy simulation.
Best Practices
- Define entities, resources, process flow, assumptions, and metrics before writing simulation logic.
- Use context managers for resource requests so resources are released reliably.
- Validate simple cases against expected results before running large scenario studies.
Avoid
- Do not mix blocking Python sleeps with SimPy process timing.
- Do not reuse triggered events when repeated signaling needs fresh events.
- Do not interpret one random run as a stable result without replications.
Frequently Asked Questions
What is SimPy used for?
SimPy models systems where events, queues, shared resources, and process timing affect outcomes.
Does this skill include runnable code?
Yes. It includes Python templates and monitoring helpers that can be run in an environment with SimPy installed.
Can it model priority queues?
Yes. The references cover PriorityResource and PreemptiveResource patterns for priority and interruption behavior.
Can it export simulation results?
Yes. The monitoring helper can export CSV data to a path supplied by the user or calling code.
Is it suitable for continuous differential equation models?
No. It is for discrete-event simulations. Continuous models usually need ODE or numerical simulation tools.
Which AI coding tools can use this skill?
The report lists support for Claude, Codex, and Claude Code.
Developer Details
Author
K-Dense-AILicense
MIT license
Skillstore revision
r1
Version notice
The author did not declare a version.
Ref
26421118b848d9f1efc0aa169d8a7a9e7e0a877e
Maintenance freshness
7/18/2026
Usage
11 downloads · 297 views
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