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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.

60

Quality

70%

Does it follow best practices?

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SecuritybySnyk

Passed

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

Quality

Content

72%Weight 40%Scale 1-3

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

The body is highly actionable with broad executable coverage and a clean reference split, but is verbose (re-explaining known concepts and duplicating reference content) and lacks explicit validation checkpoints for destructive/batch operations.

Suggestions

Trim introductory sentences that re-explain what Zarr/libraries are; assume Claude's competence and keep only what is non-obvious.

Add explicit validation steps for destructive or batch operations, e.g. verify array integrity after consolidate_metadata or re-open after migration before discarding v2 metadata.

De-duplicate content that already lives in references/api_reference.md and v3_migration.md, keeping the body a lean overview pointing to them.

DimensionReasoningScore

Conciseness

The ~770-line body re-explains basics Claude already knows ("Zarr is a Python library for storing large N-dimensional arrays...") and inlines content duplicated in the reference files; useful but could be tightened considerably.

2 / 3

Actionability

Extensive copy-paste-ready executable code across creation, indexing, compression, cloud stores, and Dask/Xarray integration, with specific kwargs and commands.

3 / 3

Workflow Clarity

The patterns/issues sections give diagnosis-to-solution sequencing, but destructive/batch operations (consolidate_metadata, migration, cloud writes) lack explicit validation checkpoints or feedback loops, capping this at 2.

2 / 3

Progressive Disclosure

A clear overview with a bundled-references table linking two real one-level-deep files (api_reference.md, v3_migration.md) and content appropriately split between body and references.

3 / 3

Total

10

/

12

Passed

Description

67%Weight 40%Scale 1-3

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 clearly scoped to Zarr-Python 3 with concrete capability list, but lacks an explicit "Use when..." trigger clause and misses some plain-language trigger terms a user would say.

Suggestions

Add an explicit trigger clause, e.g. "Use when working with Zarr arrays, chunked array storage, or cloud-native scientific datasets."

Include plainer user-facing terms like "Zarr arrays" and "chunked array storage" alongside the library/ecosystem names.

Consider trimming the parenthetical ecosystem list to keep the description tight while preserving triggers.

DimensionReasoningScore

Specificity

"Chunked N-D arrays", "Compressed arrays, parallel I/O, S3/GCS via fsspec" lists multiple concrete capabilities rather than vague language, matching the top anchor.

3 / 3

Completeness

The "what" is clear, but "when" is only implied via "for large-scale scientific computing pipelines" with no explicit "Use when..." trigger clause, which caps completeness at 2 per the guidelines.

2 / 3

Trigger Term Quality

Relevant terms (Zarr-Python 3, S3/GCS, NumPy/Dask/Xarray) appear, but plainer user phrasings like "zarr arrays" or "chunked array storage" are missing, so coverage is incomplete.

2 / 3

Distinctiveness Conflict Risk

Zarr-specific niche with distinct, library-name triggers makes it unlikely to fire for unrelated skills.

3 / 3

Total

10

/

12

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (780 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

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

Table of Contents

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