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.

Quality

83%

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Securityby

Passed

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Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is well-structured, highly actionable, and exemplifies progressive disclosure: a runnable minimal model, concrete CLI/test commands, a validated multi-step workflow, and cleanly separated reference files. Only minor conciseness trimming would improve it.

Dimension	Reasoning	Score
Conciseness	The body is dense but almost every section earns its place for a complex scientific skill, assuming Claude knows Python; minor trimming is possible (e.g., the self-referential "Citing Scientific Agent Skills" block and some repeated methodology caveats), so it sits just below fully lean.	4 / 5
Actionability	The minimal bounded model is complete and copy-paste runnable, the CLI invocations are concrete with `--help` examples, and the pinned `uv run` test command is fully executable — covering the common cases with ready-to-use code and commands.	5 / 5
Workflow Clarity	The nine-step "Model workflow" is clearly sequenced with explicit validation checkpoints ("Verify and validate", "Run independent replications", "Report limitations") and a dedicated Testing section with a feedback command, satisfying the validation requirement for batch/stochastic operations.	5 / 5
Progressive Disclosure	SKILL.md is a concise overview that points to eight verified one-level-deep `references/.md` files and seven `scripts/.py` files, all clearly signaled inline and in a References section, with details appropriately split out and easy to navigate.	5 / 5
	Total	19 / 20 Passed

Description

75%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific, concrete, and clearly niched to SimPy discrete-event simulation, but it omits an explicit "Use when..." trigger clause, which caps its completeness and slightly limits trigger-term naturalness.

Suggestions

Add an explicit "Use when..." clause naming the natural trigger situations (e.g., modeling queues, production lines, logistics, or service systems as discrete-event simulations with SimPy).

Include a couple of plain-language synonyms users might say (e.g., "event-driven simulation", "queuing model", "process simulation") to broaden trigger-term coverage.

Dimension	Reasoning	Score
Specificity	"Build, inspect, test, and analyze" lists four concrete verbs, scoped to "bounded process-based discrete-event simulations with SimPy" and enumerating "events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis" — multiple specific concrete actions with comprehensive coverage.	5 / 5
Completeness	The "what" is clear and detailed, but there is no explicit "Use when..." trigger clause; per the rubric cap, a missing explicit trigger guidance caps completeness at 3, with "when" only weakly implied by the domain wording.	3 / 5
Trigger Term Quality	Natural domain terms ("discrete-event simulations", "SimPy", "events", "resources", "interrupts", "replications", "warm-up") are well covered, but it lacks plain "Use when..." phrasing and a few common synonyms a user might say, stopping just short of comprehensive.	4 / 5
Distinctiveness Conflict Risk	"bounded process-based discrete-event simulations with SimPy" carves out a clear, narrow niche with distinct triggers and minimal overlap risk against other skills.	5 / 5
	Total	17 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: K-Dense-AI/scientific-agent-skills
Path: skills/simpy/SKILL.md
Commit: 1e5eeff

Reviewed: 3 days ago

Table of Contents

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