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.
85
73%
Does it follow best practices?
Impact
92%
1.73xAverage score across 6 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/simpy/SKILL.mdQuality
Discovery
89%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, clearly specifying both what the skill does and when to use it. Its main weakness is that it describes the framework conceptually rather than listing specific concrete actions the skill performs (e.g., 'model queuing systems', 'track resource utilization', 'run Monte Carlo simulations'). Overall it would perform well in skill selection among a large set of skills.
Suggestions
Add specific concrete actions the skill performs, e.g., 'Model queuing systems, simulate resource contention, track utilization metrics, and run replicated experiments' 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 'model resource contention', 'generate simulation statistics', or 'visualize queue lengths'. It describes what the framework is rather than what concrete actions it performs. | 2 / 3 |
Completeness | Clearly answers both 'what' (process-based discrete-event simulation framework in Python) and 'when' with an explicit 'Use this skill when...' clause listing specific trigger scenarios like manufacturing systems, service operations, network traffic, logistics, and shared resource interactions. | 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', 'discrete-event', 'processes', 'time-based events'. These are terms users would naturally use when requesting this type of work. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche — discrete-event simulation with specific domain examples is unlikely to conflict with other skills. The combination of 'simulation', 'queues', 'resources', and 'time-based events' creates a clear and unique trigger profile. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
57%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is highly actionable with excellent executable code examples and good progressive disclosure through well-signaled references. However, it is significantly too verbose for a skill file—it explains concepts Claude already knows (what SimPy is, what discrete-event simulation means, when to use it, example use cases) and could be dramatically condensed. The workflow section would benefit from explicit validation checkpoints.
Suggestions
Remove the 'When to Use This Skill', 'Overview' explanation of what SimPy is, 'Example Use Cases' section, and 'Common Pitfalls' descriptions that Claude already knows—focus only on patterns and executable guidance.
Cut the 'Core Concepts' section significantly: Claude knows what environments, processes, and events are—just show the SimPy-specific API patterns in a compact reference table or minimal code blocks.
Add an explicit validation checkpoint in the workflow, e.g., 'Run a minimal test case and compare against hand-calculated expected values before scaling up the simulation.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose for a skill targeting Claude. Explains what SimPy is, what discrete-event simulation means, lists 'when to use' and 'not suitable for' scenarios, defines basic concepts like environments and processes that Claude already knows, and includes a lengthy 'Example Use Cases' section that adds no actionable value. The content could be cut by 60%+ without losing utility. | 1 / 3 |
Actionability | The code examples are fully executable, copy-paste ready Python with proper imports. Patterns like customer-server queue, producer-consumer, and parallel tasks are complete and runnable. The workflow steps include concrete code snippets. | 3 / 3 |
Workflow Clarity | The 4-step workflow (Define System → Implement → Monitor → Analyze) provides a reasonable sequence, but lacks validation checkpoints. There's no explicit verification step to confirm the simulation is producing correct results before analysis, and the validation best practice ('compare simple cases with analytical solutions') is buried in a list rather than integrated into the workflow. | 2 / 3 |
Progressive Disclosure | Well-structured with a clear overview, quick start, then progressive depth. References to external files (references/resources.md, references/monitoring.md, etc.) are clearly signaled and one level deep. Scripts are documented with usage examples inline while pointing to separate files for full implementation. | 3 / 3 |
Total | 9 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 10 / 11 Passed | |
- Repository
- K-Dense-AI/claude-scientific-skills
- Commit
086de41
- Reviewed
Table of Contents
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