fluidsim
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
68
82%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%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 that clearly defines a specific domain (computational fluid dynamics), lists concrete capabilities and methods, and includes an explicit 'Use when...' clause with rich, natural trigger terms. The description is concise yet comprehensive, uses proper third-person voice, and occupies a distinct niche that minimizes conflict risk with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions and capabilities: Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, turbulence analysis, vortex dynamics, geophysical flows, pseudospectral methods with FFT, HPC support, and output analysis. | 3 / 3 |
Completeness | Clearly answers both 'what' (computational fluid dynamics simulations framework with pseudospectral methods, FFT, HPC, output analysis) and 'when' (explicit 'Use when...' clause listing specific trigger scenarios like running fluid dynamics simulations, Navier-Stokes, shallow water equations, turbulence analysis, etc.). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms a user would say: 'fluid dynamics simulations', 'Navier-Stokes', 'shallow water equations', 'turbulence', 'vortex dynamics', 'geophysical flows', 'CFD', 'pseudospectral', 'FFT', 'HPC'. These are the exact terms domain users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche — computational fluid dynamics is a very specific domain. The trigger terms (Navier-Stokes, shallow water equations, pseudospectral methods, geophysical flows) are unlikely to conflict with any other skill. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with comprehensive executable examples covering multiple FluidSim solvers and use cases. Its main weaknesses are verbosity (redundant content between sections, some unnecessary explanatory text) and the lack of validation/error-handling guidance in workflows—important for computationally expensive simulations. The progressive disclosure structure is well-designed in theory but the referenced files don't exist in the bundle.
Suggestions
Add validation checkpoints to the workflow: verify parameter sanity before running (e.g., check CFL conditions, resolution vs viscosity), check for simulation divergence during execution, and verify output files exist after completion.
Move the 'Common Use Cases' section to a referenced file (e.g., references/examples.md) to reduce the main SKILL.md length and improve progressive disclosure.
Remove the 'Quick Reference' section—it duplicates information already clearly presented in the workflow steps above and adds ~15 lines of redundant content.
Provide the referenced bundle files (references/installation.md, references/solvers.md, etc.) or remove the references to avoid broken links.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-structured but includes some redundancy—the 5-step workflow is repeated almost verbatim in the 'Common Use Cases' section, and the 'Quick Reference' section largely duplicates information already shown. Some explanatory text like 'delivering performance comparable to Fortran/C++' and 'Python's ease of use' is unnecessary filler for Claude. | 2 / 3 |
Actionability | The skill provides fully executable, copy-paste-ready Python code for multiple scenarios including installation commands, simulation setup, parameter configuration, and analysis. The code examples are concrete with specific values and complete enough to run. | 3 / 3 |
Workflow Clarity | The 5-step workflow is clearly sequenced, but there are no validation checkpoints or error recovery steps. For a simulation framework where misconfiguration can waste significant compute time, there should be validation steps (e.g., checking parameter validity before running, verifying output files were created, handling simulation divergence). The stratified flow example includes `statephys_from_statespect()` without explaining when/why it's needed. | 2 / 3 |
Progressive Disclosure | The skill references six separate reference files with clear descriptions, which is good structure. However, no bundle files are provided, so these references are broken/unverifiable. The main SKILL.md itself is quite long (~200+ lines) with substantial inline content that could have been pushed to reference files, particularly the 'Common Use Cases' section which contains four full examples. | 2 / 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 | |
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- K-Dense-AI/claude-scientific-skills
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