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dask

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

63

Quality

73%

Does it follow best practices?

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tessl review fix ./bundled/skills/dask/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%

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 into real reference files. It is somewhat verbose and the batch workflows lack explicit validation checkpoints, which are the main scoring drags.

Suggestions

Trim the per-component 'Reference Documentation ... which includes:' bullet lists since that material lives in the reference files; keep a one-line pointer instead.

Add explicit validation checkpoints to batch/multi-file workflows (e.g. verify row counts or schema after read, validate before to_parquet) to support validate->fix->retry loops.

De-duplicate repeated reminders like 'Operations are lazy until compute()' that appear in multiple component sections.

DimensionReasoningScore

Conciseness

The body is mostly efficient with concrete examples, but ~450 lines includes redundancy such as per-component 'Reference Documentation ... which includes:' bullet lists that duplicate reference-file contents and repeated 'Operations are lazy until compute()' reminders.

2 / 3

Actionability

Each component ships copy-paste-ready executable code (e.g. dd.read_csv with glob, da.random.random with chunks, client.map/gather) plus concrete do/don't examples for performance rules.

3 / 3

Workflow Clarity

Workflows like the ETL pipeline and iterative debugging section are sequenced, but batch/multi-file operations lack explicit validation checkpoints or validate->fix->retry feedback loops, capping workflow clarity at 2.

2 / 3

Progressive Disclosure

Clear overview with well-signaled one-level-deep references to real files (references/dataframes.md, arrays.md, bags.md, futures.md, schedulers.md, best-practices.md), each summarized and listed again in a final Reference Files section.

3 / 3

Total

10

/

12

Passed

Description

75%

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 trigger-rich, naming concrete capabilities and natural user terms. Its main weakness is the lack of an explicit 'Use when...' clause, leaving the invocation context implied rather than stated.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when datasets exceed available RAM, pandas/NumPy operations need parallelization, or workloads span multiple files/machines.'

Tighten distinctiveness by contrasting with adjacent tools (e.g. 'for larger-than-RAM datasets where you want pandas/NumPy-compatible APIs') to reduce overlap with Ray/Spark skills.

DimensionReasoningScore

Specificity

Lists multiple concrete actions such as 'Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs' rather than vague language.

3 / 3

Completeness

Clearly states what it does but the 'when' is only implied via the trailing 'for larger-than-RAM datasets and parallel workflows' clause; no explicit 'Use when...' trigger guidance, capping completeness at 2 per the rubric guideline.

2 / 3

Trigger Term Quality

Covers natural terms users would say — 'pandas/NumPy', 'parallel DataFrames/Arrays', 'multi-file processing', 'larger-than-RAM datasets', 'parallel workflows'.

3 / 3

Distinctiveness Conflict Risk

The pandas/NumPy beyond-memory framing is a fairly specific niche, but 'parallel/distributed computing' and 'parallel workflows' broadly overlap with sibling skills such as Ray, Spark, or multiprocessing.

2 / 3

Total

10

/

12

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
foryourhealth111-pixel/Vibe-Skills
Reviewed

Table of Contents

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