CtrlK
BlogDocsLog inGet started
Tessl Logo

polars

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

68

Quality

83%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 body is a well-structured, highly actionable Polars reference with strong progressive-disclosure signaling. It is held back by some conceptual over-explanation and by referencing files that do not exist in the bundle.

Suggestions

Trim conceptual explanations Claude already knows (e.g. the 'Expressions are the fundamental building blocks...' paragraph and the eager/lazy definitions) to lean on code and brief pointers instead.

Either add the referenced files under references/ (core_concepts.md, operations.md, pandas_migration.md, io_guide.md, transformations.md, best_practices.md) or remove the inline 'load references/...' pointers and Resources list so signaled paths actually resolve.

Replace placeholder snippets like `lf.filter(...).select(...).collect()` with concrete runnable examples to keep every code block fully executable.

DimensionReasoningScore

Conciseness

Mostly efficient with tight code blocks, but several sections re-explain concepts Claude already knows ('Expressions are the fundamental building blocks...', 'Eager (DataFrame): Operations execute immediately'), which is unnecessary padding.

3 / 5

Actionability

Extensive copy-paste-ready, executable Polars code covering select, filter, with_columns, group_by, joins, concat, and I/O across the common cases, with only minor placeholder use.

5 / 5

Workflow Clarity

Content is clearly sequenced by increasing complexity (Quick Start -> Core Concepts -> Operations -> Advanced) but lacks explicit validation checkpoints; acceptable since this is a reference skill rather than a destructive workflow.

4 / 5

Progressive Disclosure

Body signals one-level-deep references well and lists them with descriptions in a Resources section, but the referenced files in references/ are not actually present in the bundle, so navigation would break.

4 / 5

Total

16

/

20

Passed

Description

92%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 strong: it states concrete capabilities, gives explicit use-when guidance with natural trigger phrases, and includes a clear scale boundary that reduces conflict risk. Its only weakness is a touch of technical jargon and a few missing natural keyword variations.

DimensionReasoningScore

Specificity

Names the domain plus multiple concrete capabilities ('Lazy evaluation, parallel execution, Apache Arrow backend', 'ETL pipelines, faster pandas replacement') and explicit scale boundaries, giving comprehensive coverage rather than the minor-gaps level of 4.

5 / 5

Completeness

Explicitly answers both 'what' (in-memory DataFrame library with named capabilities) and 'when' ('Use when pandas is too slow but data still fits in memory') with concrete trigger phrases and a handoff boundary.

5 / 5

Trigger Term Quality

Strong natural trigger phrases ('pandas is too slow', 'ETL pipelines', 'faster pandas replacement') with synonyms, but missing a few natural variations users might say and leans on technical jargon ('Apache Arrow backend').

4 / 5

Distinctiveness Conflict Risk

Clear niche (in-RAM fast DataFrame, pandas replacement) with an explicit handoff to dask/vaex for larger-than-RAM data, actively minimizing overlap with out-of-core skills.

5 / 5

Total

19

/

20

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

referenced_paths_exist

Referenced path issues: 6 missing

Warning

Total

13

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.