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.
Install with Tessl CLI
npx tessl i github:K-Dense-AI/claude-scientific-skills --skill polarsOverall
score
99%
Does it follow best practices?
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npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that clearly identifies Polars (implied) as a DataFrame library, specifies its sweet spot (1-100GB datasets where pandas is too slow), and provides explicit guidance on when to use it versus alternatives. The description uses proper third-person voice, includes natural trigger terms users would actually say, and clearly distinguishes itself from competing tools.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete capabilities: 'in-memory DataFrame library', 'lazy evaluation', 'parallel execution', 'Apache Arrow backend', 'ETL pipelines', 'faster pandas replacement' with clear size guidance '1-100GB datasets'. | 3 / 3 |
Completeness | Clearly answers both what ('Fast in-memory DataFrame library with lazy evaluation, parallel execution, Apache Arrow backend') and when ('Use when pandas is too slow but data still fits in memory', '1-100GB datasets') with explicit trigger guidance. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'pandas is too slow', 'DataFrame', 'ETL pipelines', 'pandas replacement', 'larger-than-RAM', plus mentions alternative tools (dask, vaex) users might compare against. | 3 / 3 |
Distinctiveness Conflict Risk | Very distinct niche carved out: specifically positioned between pandas (slower) and dask/vaex (larger-than-RAM), with clear size boundaries (1-100GB) and specific use case (pandas too slow but data fits in memory). | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
N/AReviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
Something went wrong
Validation
94%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 15 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
metadata_version | 'metadata.version' is missing | Warning |
Total | 15 / 16 Passed | |
- Reviewed
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