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zarr-python

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

47

Quality

51%

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 ./bundled/skills/zarr-python/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

42%

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, but it is verbose and inlines a large API reference that overlaps an existing, never-referenced bundle file. Workflow guidance lacks explicit validation checkpoints for risky batch/concurrent operations.

Suggestions

Move the inline API reference and redundant examples into references/api_reference.md and replace them with concise one-level-deep links from the body.

Add explicit validation/verification checkpoints (e.g., verify consolidation freshness, check chunk alignment before bulk writes) to the concurrent-write and cloud-workflow sections.

Trim repeated chunking/compression guidance and remove explanations of concepts Claude already knows to reduce token cost.

DimensionReasoningScore

Conciseness

The ~770-line body is verbose, restates compression/chunking guidance repeatedly, explains concepts Claude already knows, and inlines a large API reference, matching the padded/unnecessary-context anchor.

1 / 3

Actionability

Provides concrete, copy-paste-ready executable code with real function names, parameters, and realistic examples (create_array, codecs, s3fs stores, Dask/Xarray integration).

3 / 3

Workflow Clarity

Mostly a reference catalog rather than a sequenced process; the performance checklist and Common Issues sections give partial sequence, but there are no explicit validation checkpoints or feedback loops for risky operations like concurrent writes and metadata-consolidation staleness.

2 / 3

Progressive Disclosure

A bundle file references/api_reference.md exists but the body never signals or links to it, and the body itself is a monolithic inline API reference duplicating what belongs in the reference — an orphaned, poorly structured bundle.

1 / 3

Total

7

/

12

Passed

Description

60%

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 capability-rich but lacks an explicit "Use when..." trigger clause and omits the product name, weakening completeness and distinctiveness. It reads as a feature list rather than trigger guidance.

Suggestions

Add an explicit "Use when..." clause naming natural triggers (e.g., storing/reading chunked arrays, cloud array storage, S3/GCS Zarr stores, large scientific datasets).

Include the name "Zarr" in the description so it is clearly distinguishable from other array-storage skills.

Add user-natural phrasing such as "Zarr arrays", ".zarr files", or "chunked array storage" to broaden trigger-term coverage.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — "Chunked N-D arrays", "Compressed arrays", "parallel I/O", "S3/GCS integration", "NumPy/Dask/Xarray compatible" — matching the multiple-specific-actions anchor.

3 / 3

Completeness

Clearly states what the skill does but provides no explicit "when to use it" trigger; per the rubric, a missing "Use when..." clause caps completeness at 2.

2 / 3

Trigger Term Quality

Contains relevant domain keywords ("cloud storage", "S3/GCS", "parallel I/O", "scientific computing pipelines") but leans technical and omits the natural "Use when..." phrasing and common layperson variations.

2 / 3

Distinctiveness Conflict Risk

The capability set is fairly niche, yet the description never names "Zarr" and reads as a generic cloud-array-storage description that could overlap with similar array-store skills.

2 / 3

Total

9

/

12

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (777 lines); consider splitting into references/ and linking

Warning

metadata_version

'metadata.version' is missing

Warning

Total

14

/

16

Passed

Repository
foryourhealth111-pixel/Vibe-Skills
Reviewed

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