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

76%

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Securityby

Passed

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tessl review fix ./skills/simpy/SKILL.md

Quality

Content

78%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 a well-structured, technically precise overview with strong progressive disclosure (all referenced bundle files are real and one level deep) and a sequenced workflow with explicit validation. The main gaps are partial code snippets for some topics and implicit rather than explicit feedback loops in the workflow.

Suggestions

Make the workflow's feedback loop explicit in step 7, e.g. "If a test or benchmark fails, fix the model and re-run validation before proceeding to replications."

Add one complete runnable snippet for interrupts or monitoring (alongside the queue model) so the highest-risk topics are as actionable as the minimal bounded model.

Trim the citing section to the essential citation plus the version rule to recover a few tokens without losing required guidance.

Dimension	Reasoning	Score
Conciseness	The body is information-dense and focuses on SimPy-specific semantics Claude would not reliably know (e.g. 4.1.2 step() callback rescheduling, ConditionValue membership), with only minor prose that could be trimmed (e.g. the citing boilerplate), placing it just below the lean/every-token-earns-its-place anchor.	4 / 5
Actionability	It provides a complete, copy-paste-ready bounded model and concrete CLI commands with --help examples, but several topics (interrupts, monitoring, conditions) rely on partial snippets rather than fully executable examples, leaving minor gaps.	4 / 5
Workflow Clarity	The 9-step Model workflow is clearly sequenced with an explicit verify/validate step (step 7) and limitations-reporting step (9), but explicit validate→fix→retry feedback loops are only implicit, so it stops short of the anchor above.	4 / 5
Progressive Disclosure	SKILL.md is a clear overview pointing to 8 reference files and 4 scripts, all of which exist; references are one level deep, signaled inline ("See references/X.md") and again in a dedicated References section with one-line descriptions, enabling easy navigation.	5 / 5
	Total	17 / 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, action-oriented, and clearly distinctive for the SimPy discrete-event-simulation niche. Its main weakness is the absence of an explicit "Use when…" trigger clause, which caps completeness at 3 despite strong specificity and distinctiveness.

Suggestions

Append an explicit trigger clause, e.g. "Use when building or analyzing process-based discrete-event simulations with SimPy, queueing/production/logistics models, or replication-based output analysis."

Add common synonyms users might say ("DES", "queueing model", "Monte Carlo simulation", "process simulation") to broaden natural-language trigger coverage.

Keep the current concrete action list — it is comprehensive and should be preserved when adding the trigger clause.

Dimension	Reasoning	Score
Specificity	The description lists multiple concrete actions ("Build, inspect, test, and analyze") plus a comprehensive enumeration of sub-areas ("events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis"), matching the comprehensive-coverage anchor.	5 / 5
Completeness	It gives a clear "what" but contains no "Use when…" clause or equivalent explicit trigger guidance; per the rubric guideline a missing trigger clause caps completeness at 3.	3 / 5
Trigger Term Quality	It includes natural domain keywords users would say ("SimPy", "discrete-event simulations", "replications", "warm-up") but lacks synonyms (e.g. DES, queueing) and any file-extension triggers, so it sits just below the comprehensive anchor.	4 / 5
Distinctiveness Conflict Risk	"bounded process-based discrete-event simulations with SimPy" carves out a clear, library-specific niche with distinct triggers and minimal overlap risk 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: 49c6e97

Reviewed: 9 days ago

Table of Contents

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