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zarr-python

Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

64

Quality

75%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/zarr-python/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

80%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.

The content is highly actionable with executable examples and excellent progressive disclosure, but it lacks explicit validation/verification checkpoints for the destructive and cloud-I/O operations it describes.

Suggestions

Add validation steps after write/append/resize operations (e.g., reopen read-only and assert shape/dtype, or verify chunk count) to create feedback loops for batch and cloud operations.

Trim concept-explanation prose like the HDF5 comparison in the Groups section since Claude already knows these concepts.

Provide a short end-to-end workflow (create store → write → verify → read) with explicit checkpoints for cloud-backed pipelines.

DimensionReasoningScore

Conciseness

The body is mostly lean code examples with brief section intros and a compact reference list, but a few prose lines (e.g., 'Groups organize multiple arrays hierarchically, similar to directories or HDF5 groups') explain concepts Claude already knows and could be trimmed.

4 / 5

Actionability

Fully executable, copy-paste-ready code covers the common cases (create, open, read/write with indexing, resize, append, groups, attributes) with specific function calls and realistic arguments.

5 / 5

Workflow Clarity

Sections follow a logical learning sequence (install → create → operate → groups → attributes) but there are no validation checkpoints for destructive/batch operations like cloud writes and parallel I/O, which caps workflow clarity at 3.

3 / 5

Progressive Disclosure

A clear overview with well-signaled, one-level-deep references (all six referenced files exist in ./references/) and a bundled-references table; detail is appropriately split out with easy navigation.

5 / 5

Total

17

/

20

Passed

Description

70%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.

The description is specific and distinctive with strong domain keywords, but it lacks an explicit 'Use when...' trigger clause, leaving the invocation guidance only weakly implied.

Suggestions

Add an explicit trigger clause such as 'Use when working with chunked N-dimensional arrays, Zarr stores, or cloud-backed scientific array data.'

Include the .zarr file extension and a common synonym to round out trigger-term coverage.

Lead with a verb-based action (e.g., 'Store and read chunked N-D arrays...') to sharpen specificity.

DimensionReasoningScore

Specificity

Lists several concrete capabilities ('Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible') rather than vague language, though they are noun-phrase features rather than verb-based actions, leaving minor gaps.

4 / 5

Completeness

It clearly states what the skill does, but the 'when' is only weakly implied via 'for large-scale scientific computing pipelines' with no explicit 'Use when...' trigger clause, which caps completeness at 3.

3 / 5

Trigger Term Quality

Good natural-term coverage for the domain (Zarr, S3, GCS, NumPy, Dask, Xarray, chunked arrays, cloud storage) that users would actually say, but no file extension (.zarr) or common synonyms, so it is not fully comprehensive.

4 / 5

Distinctiveness Conflict Risk

Names a clear niche ('Chunked N-D arrays for cloud storage (Zarr-Python 3)') with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

16

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
K-Dense-AI/scientific-agent-skills
Reviewed

Table of Contents

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