When should I use frozen=True?

Use frozen=True for configuration objects or data records that should remain constant after creation, preventing accidental modifications and making instances hashable.

Can I validate dataclass fields during initialization?

Yes, you can use the __post_init__ method to perform validation checks on fields immediately after they are set, raising errors if parameters are out of range.

How do I fix the 'mutable default' error in dataclasses?

Avoid using mutable defaults like [] or {}. Instead, use the field(default_factory=list) or field(default_factory=dict) syntax to ensure each instance receives a fresh object.

Why should I use slots=True in Python dataclasses?

Using slots=True prevents the creation of a __dict__ for each instance, which can reduce memory usage by up to 20% and slightly improve attribute access speed.

Python Dataclass Optimizer

Name: Python Dataclass Optimizer
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Optimizes Python dataclasses for memory efficiency, immutability, and validation using advanced PEP 557 patterns.

About

This skill empowers Claude to refactor and implement high-performance Python dataclasses by applying industry-standard optimization patterns. It focuses on reducing memory overhead using slots, ensuring data integrity through immutability and post-initialization validation, and handling common pitfalls like mutable default arguments. It is particularly valuable for developers working on data-heavy applications, machine learning configurations, or robust backend systems where performance and reliability are critical.

Key Features

Prevention of shared state bugs using field(default_factory=...)
Memory footprint reduction using slots=True for faster attribute access
Immutability enforcement with frozen=True for safer configuration management
0 GitHub stars
Early error detection via __post_init__ validation logic
Equality control using compare=False to exclude metadata and timestamps

Use Cases

Building robust, immutable configuration systems for complex applications
Creating efficient Data Transfer Objects (DTOs) for high-traffic API services
Refactoring memory-intensive data models in data science and ML pipelines

About

Key Features

Prevention of shared state bugs using field(default_factory=...)
Memory footprint reduction using slots=True for faster attribute access
Immutability enforcement with frozen=True for safer configuration management
0 GitHub stars
Early error detection via __post_init__ validation logic
Equality control using compare=False to exclude metadata and timestamps

Use Cases

Building robust, immutable configuration systems for complex applications
Creating efficient Data Transfer Objects (DTOs) for high-traffic API services
Refactoring memory-intensive data models in data science and ML pipelines