Does it automatically change my code?

No. The skill follows a strict human-in-the-loop policy. It presents findings and suggested actions, then waits for explicit user approval before executing any modifications.

How does Pythonista Reviewing handle large pull requests?

The skill uses specialized scripts to filter out noisy files like lock files and integrates with large-context LLMs to analyze the entire diff in a single pass, avoiding the loss of context that comes from chunking files.

What are 'egregious issues' in the context of this skill?

It focuses on structural problems like functions over 100 lines, code duplication in two or more places, weak type annotations, and tests that provide no real validation.

What kind of issues does this skill ignore?

It intentionally ignores minor issues like variable naming, line length, whitespace, and import order, as these are better handled by automated linters and formatters.

Pythonista Code Reviewer

Name: Pythonista Code Reviewer
Author: gigaverse-app

bygigaverse-app

Collaboration Tools

Performs high-level architectural reviews of Python pull requests to identify structural flaws, code duplication, and complex 'god functions'.

About

Pythonista Code Reviewer is a specialized Claude Code skill designed to conduct deep, structural analysis of Python codebases during the review process. Unlike standard linters that focus on cosmetic formatting, this skill prioritizes egregious architectural issues such as cross-file code duplication, weak typing patterns, and oversized functions that hinder maintainability. It utilizes intelligent diff filtering to remove noise from lock files and leverages large-context LLM workflows to detect systemic patterns across entire pull requests, ensuring that developers focus on significant code quality improvements rather than minor stylistic choices.

Key Features

Human-in-the-loop workflow that requires explicit approval before applying suggested fixes
Intelligent diff filtering to exclude noise from lock files and documentation automatically
Integration with large-context LLMs for comprehensive analysis of massive PR diffs
0 GitHub stars
High-level structural analysis focusing on architectural integrity over minor linting issues
Cross-file pattern detection to identify systemic code duplication and logic repetition

Use Cases

Enforcing robust type-hinting and design patterns across multi-file code contributions
Reviewing complex pull requests to ensure structural quality and architectural consistency
Identifying and refactoring 'god functions' and dead code in legacy Python projects

About

Key Features

Human-in-the-loop workflow that requires explicit approval before applying suggested fixes
Intelligent diff filtering to exclude noise from lock files and documentation automatically
Integration with large-context LLMs for comprehensive analysis of massive PR diffs
0 GitHub stars
High-level structural analysis focusing on architectural integrity over minor linting issues
Cross-file pattern detection to identify systemic code duplication and logic repetition

Use Cases

Enforcing robust type-hinting and design patterns across multi-file code contributions
Reviewing complex pull requests to ensure structural quality and architectural consistency
Identifying and refactoring 'god functions' and dead code in legacy Python projects