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

Low

Low-risk findings worth noting

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.

A lean, highly actionable SimPy skill body with executable examples, explicit validation in its multi-step workflow, and excellent one-level reference structure; the only drag is inline version/date pinning that could be isolated.

Dimension	Reasoning	Score
Conciseness	The body is information-dense and assumes Python/SimPy competence with little padding, but inline time-sensitive details (verification date, PyPI release date, version pin) are not confined to a deprecated/old-patterns section, which the rubric penalizes slightly.	4 / 5
Actionability	Provides a fully executable minimal bounded model, concrete CLI invocations with --help examples, and a copy-paste pinned test command covering the common cases.	5 / 5
Workflow Clarity	The 9-step Model workflow is clearly sequenced with explicit validation checkpoints ('Bound execution', 'Verify and validate', 'Run independent replications') and feedback guidance for risky unbounded runs, satisfying the batch-operation validation requirement.	5 / 5
Progressive Disclosure	SKILL.md is a clear overview with well-signaled one-level-deep references (events.md, resources.md, monitoring.md, etc., all present in the bundle) and a consolidated References section, giving easy navigation.	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.

A specific, distinctive, third-person description that comprehensively names capabilities and the domain, but it lacks any explicit 'when to use' trigger guidance, which caps completeness.

Suggestions

Add a 'Use when ...' clause naming natural trigger phrases (e.g., 'Use when building queue, production, logistics, or service-operation simulations, or when the user mentions discrete-event simulation or SimPy').

Include common user synonyms like 'queueing model' and 'event-driven simulation' alongside 'discrete-event simulation' to broaden trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ("Build, inspect, test, and analyze") plus a comprehensive feature inventory (events, resources, interrupts, monitoring, replications, warm-up, output analysis), matching the comprehensive-coverage anchor.	5 / 5
Completeness	The 'what' is clear and detailed, but there is no 'Use when...' clause or equivalent explicit trigger guidance, which per the rubric caps completeness at 3.	3 / 5
Trigger Term Quality	Natural terms like "SimPy", "process-based discrete-event simulations", and feature nouns are present, but common user phrasings such as "queueing model" or "event-driven simulation" are missing, so it is just below comprehensive.	4 / 5
Distinctiveness Conflict Risk	Tightly scoped to SimPy and bounded process-based discrete-event simulation, a clear niche with distinct triggers and minimal overlap with 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: b2a92ba

Reviewed: 3 days ago

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