simpy

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

Quality

85%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

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.

A thorough, highly actionable skill body with excellent progressive disclosure and executable examples. The main weakness is verbosity: re-explaining familiar programming concepts and repeating boilerplate inflates the token budget without adding value.

Suggestions

Trim explanations of concepts Claude already knows (e.g., 'Processes are defined using Python generator functions (functions with yield statements)') and consolidate the repeated `import simpy` / environment-creation boilerplate across examples.

Fold the validation guidance ('Compare simple cases with analytical solutions') into the Run-and-Analyze workflow step as an explicit checkpoint rather than relegating it to Best Practices.

The 'Core capabilities' list in the Overview overlaps with the 'When to Use This Skill' list; merge or cross-reference them to avoid redundant content.

Dimension	Reasoning	Score
Conciseness	Mostly efficient with plentiful code examples, but the ~420-line body re-explains concepts Claude already knows (what a generator/yield/process/event is) and repeats import boilerplate and capabilities across overlapping sections.	3 / 5
Actionability	Provides copy-paste-ready, executable code across Quick Start, resource usage, three common patterns, and a workflow guide, plus importable scripts that cover the common simulation cases.	5 / 5
Workflow Clarity	A clear four-step workflow (Define → Implement → Monitor → Run/Analyze) is well sequenced, but explicit validation checkpoints are not embedded inline; the validation guidance lives separately in Best Practices rather than as a workflow step.	4 / 5
Progressive Disclosure	The body is a clear overview that points to five real one-level-deep reference files and two scripts, with a dedicated Reference Documentation section and well-signaled inline links.	5 / 5
	Total	17 / 20 Passed

Description

92%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 strong, well-constructed description that clearly conveys what the skill does and when to use it, with concrete domain examples and low conflict risk. Trigger-term coverage is good but could add a few more everyday synonyms.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete capabilities — 'processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics' — giving comprehensive coverage of the domain.	5 / 5
Completeness	Explicitly states both what it is ('Process-based discrete-event simulation framework in Python') and when to use it ('Use this skill when building simulations of systems with...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural keyword coverage ('simulations', 'manufacturing systems', 'service operations', 'network traffic', 'logistics', 'queues', 'resources') with relevant synonyms, though a few common phrasings users might say are absent.	4 / 5
Distinctiveness Conflict Risk	Occupies a clear niche (discrete-event simulation) with distinct, specific triggers that are unlikely to fire for unrelated skills.	5 / 5
	Total	19 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/coding/simpy/SKILL.md
Commit: 3a6c3a9

Reviewed: 1 day ago

Table of Contents

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