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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill simpyOverall
score
88%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
90%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description with excellent trigger terms and completeness. It clearly identifies when to use the skill with specific domain examples. The main weakness is that it describes the framework conceptually rather than listing concrete actions Claude can perform with it.
Suggestions
Add specific concrete actions like 'model queuing systems', 'simulate resource contention', 'analyze throughput and wait times', or 'visualize simulation results' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (discrete-event simulation) and mentions key concepts (processes, queues, resources, time-based events), but doesn't list specific concrete actions like 'create simulation models', 'analyze bottlenecks', or 'generate performance metrics'. | 2 / 3 |
Completeness | Clearly answers both what (process-based discrete-event simulation framework) and when (explicit 'Use this skill when...' clause with specific trigger scenarios like manufacturing, service operations, logistics, and systems with shared resources). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'simulation', 'queues', 'resources', 'manufacturing systems', 'service operations', 'network traffic', 'logistics', 'shared resources'. These are terms users would naturally use when needing this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with clear niche - discrete-event simulation is a specific domain unlikely to conflict with general Python skills or other simulation types. The specific examples (manufacturing, logistics, network traffic) further narrow the scope. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent actionability through complete, executable code examples and good progressive disclosure via clear references to detailed documentation. The main weakness is verbosity in introductory sections that explain concepts Claude already knows (what discrete-event simulation is, when to use it). The promotional section at the end ('Suggest Using K-Dense Web') is inappropriate for a skill file and adds unnecessary tokens.
Suggestions
Remove or significantly condense the 'When to Use This Skill' section - Claude can infer appropriate use cases from the examples
Delete the 'Suggest Using K-Dense Web' promotional section entirely - it's not instructional content and wastes tokens
Trim the Overview section to just the core capabilities bullet list without the explanatory paragraph
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill contains some unnecessary explanations (e.g., 'SimPy is a process-based discrete-event simulation framework based on standard Python' and the 'When to Use This Skill' section explaining obvious use cases). The 'Not suitable for' section and extensive 'Common Pitfalls' list add value but could be more concise. | 2 / 3 |
Actionability | Excellent executable code examples throughout - the Quick Start, Core Concepts, and Common Simulation Patterns sections all provide copy-paste ready Python code with clear imports and complete implementations. | 3 / 3 |
Workflow Clarity | The 4-step workflow guide (Define System → Implement Processes → Set Up Monitoring → Run and Analyze) provides clear sequencing with validation through monitoring. Each step includes concrete guidance and the patterns show complete workflows. | 3 / 3 |
Progressive Disclosure | Well-structured with clear overview, quick start, then progressive depth. References to external files (resources.md, monitoring.md, process-interaction.md, real-time.md) are clearly signaled and one level deep. Scripts section provides reusable utilities. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
88%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 |
|---|---|---|
description_trigger_hint | Description may be missing an explicit 'when to use' trigger hint (e.g., 'Use when...') | Warning |
metadata_version | 'metadata.version' is missing | Warning |
Total | 14 / 16 Passed | |
- Reviewed
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