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.

Quality

93%

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Securityby

Low

Low-risk findings worth noting

Quality

Content

87%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A lean, highly actionable single-file skill with executable code and compact high-signal tables. The main gap is the absence of explicit validation checkpoints in the load workflow, which the rubric penalizes for batch/risky operations.

Suggestions

Add an explicit verification checkpoint after loading — e.g., assert the expected [N, X, T, C] shape and dtype before returning data, and re-download on size mismatch — to close the workflow-validation gap.

Insert a short validation step in the DaRUS download flow (check file size / md5 against the CSV after aria2c) so partial downloads are caught as a checkpoint rather than only handled reactively in the pitfalls table.

Consider splitting the full load_pde_hdf5 implementation or DaRUS sourcing detail into a references/ file if the skill grows, keeping SKILL.md as an overview pointing one level deep.

Dimension	Reasoning	Score
Conciseness	The body is dense and high-signal: executable code, compact tables, and short notes with no explanation of concepts Claude already knows (e.g., no "what is HDF5"). Every section earns its place; it does not fall to the score-2 'could be tightened' band.	3 / 3
Actionability	Provides fully executable, copy-paste-ready code (load_pde_hdf5 loader, hf_hub_download with concrete repo_id/filename, aria2c download command, PDEDS Dataset and DataLoader) plus specific downsample factors — matching the score-3 anchor for concrete, executable guidance.	3 / 3
Workflow Clarity	Sections are logically sequenced (detect layout → load → source data → build DataLoader → downsample) and the Common Pitfalls table gives error→fix recovery guidance, but the load workflow lacks explicit validation checkpoints (e.g., verify file integrity / shape before proceeding), which the rubric says should cap batch/risky operations at 2.	2 / 3
Progressive Disclosure	No bundle files exist and the skill is a single, well-organized file with clear sections (When to Use, Layout Detection, Data Sources, DataLoader, Downsampling, Pitfalls) and no nested references; this self-contained organization satisfies the rubric's allowance for skills with no external-reference needs.	3 / 3
	Total	11 / 12 Passed

Description

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.

A strong, specific description that uses third-person voice, names concrete capabilities, and includes an explicit 'Use when' trigger for a well-defined niche. It would not benefit from padding or vague qualifiers.

Dimension	Reasoning	Score
Specificity	Names the domain and multiple concrete actions: "layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation" — a comprehensive list of specific capabilities matching the score-3 anchor.	3 / 3
Completeness	Clearly states what it does (loading, layout detection, downsampling, multi-variable, data sources, DataLoader) and provides an explicit trigger ("Use when preparing PDE data for neural operator training."), satisfying both what and when.	3 / 3
Trigger Term Quality	Includes the natural terms a user would say for this niche — "HDF5", "PDE", "PDEBench", "PhiFlow", "JAX-CFD", "DataLoader", "neural operator training" — giving good coverage rather than generic or purely jargon phrasing.	3 / 3
Distinctiveness Conflict Risk	Occupies a clear niche (HDF5 PDE simulation data for neural operator training) with domain-specific triggers unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

93%

Warnings & errors only

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

Criteria	Description	Result
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
Commit: 52845c3

Reviewed: about 10 hours ago

Table of Contents

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