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.
8.3
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Excellent skill for Polars DataFrame operations. The description clearly positions when to use Polars (pandas too slow, data fits in RAM, 1-100GB) vs alternatives. SKILL.md is exceptionally well-structured with progressive disclosure: quick start, core concepts, common operations, and clear references to detailed docs. Task knowledge is comprehensive, covering expressions, lazy/eager evaluation, I/O, joins, aggregations, and pandas migration with executable code examples. The structure is exemplary—concise overview in main file with organized references for deeper topics. Novelty is strong: Polars' expression API and lazy evaluation patterns require significant tokens for a CLI agent to construct correctly, and this skill provides reusable patterns. Minor deduction on novelty only because basic DataFrame operations are somewhat standard across libraries, though Polars-specific optimizations (lazy evaluation, expression chaining) add meaningful complexity reduction.
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