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polars

High-performance DataFrame library for Python ETL, analytics, and pandas migration. It supports expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

56

Quality

71%

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SecuritybySnyk

Passed

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tessl review fix ./skills/polars/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%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.

A well-structured reference skill with extensive executable examples and a genuinely one-level-deep reference bundle. Main weaknesses are conceptual padding Claude does not need and validation guidance that is siloed rather than woven into the operating workflows.

Suggestions

Cut or compress the conceptual prose (the "Expressions" definition paragraph, "Benefits of lazy evaluation" bullet list, and lazy-vs-eager explanation) down to one line each; Claude already knows these concepts.

Move the "Scientific validation" checks inline to the operations they guard (e.g., put join cardinality validation next to the Joins section, schema-override guidance next to I/O) so checkpoints appear where failures occur.

Trim the inline "Common Operations" code blocks that duplicate references/operations.md, keeping one exemplar per operation in SKILL.md and pointing to the reference for the rest.

DimensionReasoningScore

Conciseness

Mostly efficient with dense code examples, but it includes unnecessary conceptual explanation Claude already knows ("Expressions are the fundamental building blocks... They describe transformations", the "Benefits of lazy evaluation" list, and "Parallel computation" commentary) that could be trimmed.

3 / 5

Actionability

The body provides abundant executable, copy-paste-ready code for the common cases, with minor gaps: placeholder fragments like `lf.filter(...).select(...).collect()` and cloud/GPU/BigQuery recipes acknowledged as illustrative rather than tested.

4 / 5

Workflow Clarity

The topical organization implies a rough sequence (install → load → transform → validate), and the "Scientific validation" section contains real checkpoints (assert_frame_equal, join validate, null/NaN counting), but they are isolated at the end rather than integrated as explicit checkpoints in each workflow step.

3 / 5

Progressive Disclosure

Well-signaled, one-level-deep references: every cited path (core_concepts.md, operations.md, pandas_migration.md, io_guide.md, transformations.md, best_practices.md, review.md) exists in the bundle with no nested references, though some inline API detail in "Common Operations" duplicates content that also lives in operations.md.

4 / 5

Total

14

/

20

Passed

Description

66%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.

A specific, feature-rich description of what the library supports, but it reads as a capability list with no trigger guidance for when the skill should activate. Adding an explicit "Use when..." clause with natural user phrasing would resolve the main gap.

Suggestions

Add an explicit trigger clause, e.g. "Use when the user mentions Polars, DataFrames, migrating pandas code to Polars, or optimizing data pipeline performance."

Include the library name "Polars" and common file-format synonyms (CSV, Parquet) as natural trigger terms users would actually say.

Convert one or two feature nouns into concrete actions (e.g., "Migrate pandas pipelines to Polars", "Optimize DataFrame queries with lazy evaluation") to sharpen the action framing.

DimensionReasoningScore

Specificity

The description lists several concrete capabilities ("lazy query optimization", "streaming out-of-core processing", "Arrow interoperability", "optional GPU execution") plus named use domains ("ETL, analytics, and pandas migration"), but these are features rather than explicit actions, leaving minor gaps versus comprehensive coverage.

4 / 5

Completeness

The "what" is clearly stated (high-performance DataFrame library with enumerated capabilities), but there is no "Use when..." clause or equivalent explicit trigger guidance, which caps completeness at 3 per the judging guidelines.

3 / 5

Trigger Term Quality

Good natural keyword coverage ("DataFrame", "Python", "pandas", "ETL", "analytics"), but the library name "Polars" itself and common synonyms users would say (e.g., "data wrangling", "data pipeline", "csv", "parquet") are missing.

4 / 5

Distinctiveness Conflict Risk

The expression-based/lazy/pandas-migration framing carves a mostly distinct niche, with only minor overlap risk against a generic pandas or DataFrame-manipulation skill.

4 / 5

Total

15

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/polars/SKILL.md

Reviewed

Table of Contents

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