Dataframely: Polars Schema & Collection Best Practices

What it does

Provides concrete conventions and examples for using Dataframely to define dy.Schema and dy.Collection types that document, validate, and enforce structure in Polars data frames. The skill explains how to declare column types, constraints, cross-column rules, group rules, and how to use typed DataFrame/LazyFrame wrappers for stable interfaces and safer data transforms. It also covers validation vs. filtering semantics, synthetic test data generation, and I/O conventions for persisting schema metadata.

When to use it

Use this skill when you are writing or refactoring ETL/data-processing code that consumes or produces Polars data frames and you want strong, testable schema guarantees. It is helpful for onboarding, enforcing data contracts, validating inputs/outputs, and designing unit tests for transformations.

What's included

• Scripts: none declared in the repo snapshot (has_scripts=false).
• References: none in the fetched skill file.
• Instructions: detailed examples for defining dy.Schema subclasses, dy.Collection relationships, writing @dy.rule and @dy.filter methods, using Schema.validate vs Schema.filter, casting for performance, and recommendations for tests and synthetic data generation.

Compatible agents

Best used with coding-capable assistants that can read Python and Polars idioms (Copilot-style or Claude Code), and with CI/test automation that runs Python test suites.