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

74%

Does it follow best practices?

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Securityby

Passed

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

Quality

Content

65%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 body is a well-structured, actionable overview with excellent progressive disclosure via real reference files and scripts. It loses points on conciseness (concept re-explanation and duplicated examples) and workflow clarity (no explicit validation checkpoints in the build workflow).

Suggestions

Remove redundant explanation of basics Claude already knows (e.g. what generator functions/yield are, what an environment does) and consolidate the duplicated customer-server example into one canonical instance.

Add explicit validation checkpoints to the Workflow Guide — e.g. a Step that says 'Validate: compare results against an analytical/known-case solution before trusting outputs' — to raise workflow clarity.

Wire the monitoring/stats objects end-to-end in the Step 4 example (show where stats comes from) so the run-and-analyze code is fully copy-paste executable.

Dimension	Reasoning	Score
Conciseness	The 430-line body explains concepts Claude already knows ('Processes are defined using Python generator functions (functions with yield statements)', 'The simulation environment manages time and schedules events') and repeats material — the customer-server example appears twice (resource usage block and Pattern 1) and Resource usage is shown across the table, Quick Reference, and patterns — placing it at the 'mostly efficient but includes some unnecessary explanation / could be tightened' anchor.	3 / 5
Actionability	Multiple copy-paste-ready, executable examples cover the common cases (environment setup, resource request, customer-server, producer-consumer, parallel tasks), and referenced scripts' classes (ResourceMonitor, run_simulation) actually exist; minor gaps like the unwired stats object keep it at 4 rather than fully executable 5.	4 / 5
Workflow Clarity	The four-step Workflow Guide (Define → Implement → Monitor → Run & Analyze) gives a clear sequence with code per step, but there are no explicit validation checkpoints — verification only appears in Best Practices as advice, not as a workflow step — matching the 'steps listed but validation gaps; checkpoints implicit' anchor.	3 / 5
Progressive Disclosure	SKILL.md is a clear overview that offloads detail to well-signaled one-level-deep references (resources.md, events.md, process-interaction.md, monitoring.md, real-time.md) and real scripts, with a dedicated Reference Documentation section — all referenced files exist — so navigation is easy and content is appropriately split.	5 / 5
	Total	15 / 20 Passed

Description

83%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 strong: it explicitly answers both what the skill does and when to use it, with concrete domain scenarios and natural trigger terms. Its main gap is the absence of the library name 'SimPy' and synonyms, which slightly limits trigger-term coverage and distinctiveness.

Suggestions

Add the library name and keyword 'SimPy' directly in the description so users who mention the library by name trigger it, and to sharpen distinctiveness from generic simulation skills.

Include a common synonym or file/extension cue (e.g. 'SimPy (.py) simulations') to push trigger-term coverage toward comprehensive.

Consider tightening the long list of example domains so the description stays concise while preserving the strongest triggers.

Dimension	Reasoning	Score
Specificity	Names the domain plus several concrete concepts — 'processes, queues, resources, and time-based events' and scenarios like 'manufacturing systems, service operations, network traffic, logistics' — which matches the 'lists several specific actions; minor gaps' anchor; it is scenario-driven rather than an enumeration of distinct concrete actions, so it stays at 4 rather than 5.	4 / 5
Completeness	It clearly states what ('Process-based discrete-event simulation framework in Python') and explicitly when ('Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as...'), matching the anchor for explicit what-and-when with concrete trigger phrases.	5 / 5
Trigger Term Quality	Strong natural terms a user would actually say — 'discrete-event simulation', 'manufacturing systems', 'service operations', 'network traffic', 'logistics', 'queues', 'resources' — giving good coverage, but it omits the library name 'SimPy' and common synonyms/file extensions, so it falls just below the comprehensive 5 anchor.	4 / 5
Distinctiveness Conflict Risk	The 'discrete-event simulation' niche is clear and the entity/resource framing is specific, but the description itself never names 'SimPy', so it leans on the discrete-event qualifier to separate itself from generic simulation/Monte Carlo skills — mostly distinct with minor overlap risk.	4 / 5
	Total	17 / 20 Passed

Validation

93%

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 — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	15 / 16 Passed

Repository: foryourhealth111-pixel/Vibe-Skills
Path: bundled/skills/simpy/SKILL.md
Commit: ddcaa2a

Reviewed: 14 days ago

Table of Contents

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