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simpy

Builds, inspects, tests, and analyzes bounded process-based discrete-event simulations with SimPy. Use for event scheduling, resource queues, interrupts, monitoring, independent replications, warm-up, and reproducible output analysis.

Quality

91%

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SKILL.md
Quality
Evals
Security

Quality

Content

86%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 well-engineered body: executable code and commands throughout, a disciplined 9-step methodology workflow, and exemplary one-level reference structure. The main improvement opportunity is trimming baseline SimPy API explanation (event lifecycle basics, resource table, request pattern) that Claude already knows, and making validation feedback loops explicit.

Suggestions

Compress the 'Event, Timeout, Process, and Condition' basics and the resource-type table into the existing references (events.md, resources.md), keeping only the version-specific gotchas (ConditionValue keyed by event objects, AnyOf not canceling losers) inline.

Add an explicit feedback loop to the workflow: after step 7 (verify/validate), state that on failure Claude should fix the model and re-run the deterministic tests before proceeding to replications.

Add a one-line inline usage example for scripts/resource_monitor.py (as done for the other CLIs) so the monitoring section's primary tool is immediately runnable.

DimensionReasoningScore

Conciseness

The body is dense and mostly earns its tokens with version-specific gotchas (half-open horizon, 4.1.2 StopSimulation callback rescheduling, Preempted cause fields, time-weighted monitoring). Not 5: a few sections restate baseline SimPy knowledge Claude already has — the generic Event/Timeout/Process lifecycle, the resource-type table, and the basic request context-manager example. Not 3: these are minor, compressed instances rather than padded sections.

4 / 5

Actionability

Fully executable throughout: a copy-paste runnable bounded model, pinned uv install commands, a reproducible pytest invocation, and concrete CLI commands for all four bundled scripts. Specific examples cover the common cases (build, run, replicate, validate, test).

5 / 5

Workflow Clarity

The 9-step model workflow is clearly sequenced with an explicit verify/validate step (step 7), replication step (8), and a dedicated Testing section with a runnable command. Not 5: there is no explicit error-recovery feedback loop ("if validation fails, fix and re-run"), and checkpoint guidance for the instrumentation/monitoring steps is implied rather than spelled out.

4 / 5

Progressive Disclosure

Clear overview in SKILL.md with all 8 references and 7 scripts verified to exist, each referenced inline with purpose ("Read references/resources.md for blocked operations...") and indexed in a References section with one-line descriptions. References link only sideways to each other and sources.md — one level deep with easy navigation.

5 / 5

Total

18

/

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 third-person description with concrete verbs, an explicit "Use for..." trigger clause listing specific scenarios, and a clearly distinct SimPy niche. The only weakness is modest synonym coverage for natural trigger phrases.

DimensionReasoningScore

Specificity

"Builds, inspects, tests, and analyzes bounded process-based discrete-event simulations with SimPy" lists multiple concrete actions with a precise, qualified object ("bounded process-based discrete-event"). Not 4: coverage of what the skill does within its domain is comprehensive rather than having identifiable gaps.

5 / 5

Completeness

Explicitly answers both: what ("Builds, inspects, tests, and analyzes bounded... simulations with SimPy") and when ("Use for event scheduling, resource queues, interrupts, monitoring, independent replications, warm-up, and reproducible output analysis"). Matches the anchor-5 example structure exactly.

5 / 5

Trigger Term Quality

Includes strong natural terms a user would say: "SimPy", "event scheduling", "resource queues", "interrupts", "replications", "warm-up". Not 5: a few natural synonyms/variants are missing, e.g. "queueing network", "Monte Carlo", "simulation model".

4 / 5

Distinctiveness Conflict Risk

A clear niche (SimPy discrete-event simulation) with distinct triggers (replications, warm-up, interrupts) that no generic skill would claim. Not 4: overlap risk with adjacent skills is minimal, not minor-but-present.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Reviewed

Table of Contents

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