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hdf5-pde-data-loading

Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data for neural operator training.

70

Quality

88%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

90%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.

Excellent operational content: executable code throughout, dense reference tables, and zero padding. The only gaps are an implicit rather than explicit step sequence with no validation checkpoint after loading, and a long inline loader function that could be offloaded to a script.

DimensionReasoningScore

Conciseness

The body is lean and code-first with no filler: it never explains what HDF5 is or how the libraries work, and every section (layout detection, data sources, DataLoader, downsampling table, pitfalls table) delivers information Claude would not already know.

5 / 5

Actionability

Nearly all code is copy-paste ready and executable — the robust load_pde_hdf5 loader, hf_hub_download call, aria2c download command, and Dataset/DataLoader class — and the downsampling and pitfalls tables give concrete numbers and symptom/fix pairs.

5 / 5

Workflow Clarity

Sections follow a coherent order (layout detection → data sources → DataLoader → downsampling) and the pitfalls table plus the truncated-file re-download guidance provide error-recovery feedback, but there is no explicit ordered workflow or post-load verification checkpoint, so checkpoints remain implicit rather than explicit.

4 / 5

Progressive Disclosure

A self-contained skill with no bundle files, well-organized headed sections, and zero nested references; all inline content is core operational material, though a ~45-line robust loader could arguably live in a scripts/ file rather than inline.

4 / 5

Total

18

/

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.

A strong, third-person description with concrete, comprehensive capability statements and an explicit use-when clause. Its main weakness is trigger coverage: the when-clause is a single narrow case and file-extension synonyms (.h5/.hdf5) are absent.

Suggestions

Add file-extension synonyms to the description (e.g., '.h5/.hdf5 files') so users who name the extension rather than 'HDF5' still trigger the skill.

Broaden the use-when clause to cover more natural trigger cases, e.g. 'Use when loading or downloading PDE datasets (PDEBench, PhiFlow, JAX-CFD), reading .h5 files, or preparing simulation data for neural operator training.'

Consider mentioning grid/coordinate handling since it is a concrete capability the body documents but the description omits.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'layout detection (single tensor vs separate variables)', 'spatial/temporal downsampling', 'multi-variable systems', 'HuggingFace and DaRUS data sources', 'efficient PyTorch DataLoader creation' — with comprehensive coverage of the skill's scope, matching the anchor-5 example.

5 / 5

Completeness

Both 'what' and 'when' are explicitly present ('Use when preparing PDE data for neural operator training'), but the trigger clause covers a single narrow case and omits other natural triggers such as downloading PDEBench datasets or reading .h5 files, fitting 'when' could be more explicit or specific.

4 / 5

Trigger Term Quality

Good coverage of natural domain terms (PDEBench, PhiFlow, JAX-CFD, HDF5, PyTorch, DataLoader, downsampling, HuggingFace), but file-extension synonyms like '.h5'/'.hdf5' and common phrasings like 'load simulation data' are missing, so it falls just short of the comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

A clear niche — loading PDE simulation datasets from HDF5 for named frameworks (PDEBench, PhiFlow, JAX-CFD) — with distinct triggers and minimal overlap risk with other data-loading skills.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

synthetic-sciences/openscience

Path

backend/cli/skills/data-engineering/hdf5-pde-data-loading/SKILL.md

Reviewed

Table of Contents

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