Can this skill help improve my unit tests?

Yes, it specifically evaluates testability and flags missing coverage for critical edge cases and error handling.

How does this skill differ from a standard Python linter?

While linters focus on syntax and style, this skill evaluates high-level design, logic correctness, performance bottlenecks, and architectural maintainability.

Does it provide specific code fixes?

Yes, the review process includes explaining why an issue matters and providing concrete examples or fixes to resolve the problem.

What is the recommended workflow for using this skill?

It is best used immediately after writing new code or modifying existing functions, before the code is merged into the main repository.

Python Code Reviewer

Name: Python Code Reviewer
Author: jorijn

byjorijn

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Collaboration Tools

Conducts comprehensive, expert-level Python code reviews focused on architectural integrity, maintainability, and idiomatic correctness.

About

The Python Code Reviewer skill empowers Claude to perform high-stakes reviews of Python codebases, identifying bugs, performance bottlenecks, and design flaws before they reach production. By prioritizing structural improvements and best practices over minor style preferences, it helps developers maintain high-quality standards across refactors and new features. It provides structured feedback categorized by severity—from critical bugs to general suggestions—ensuring that the most impactful issues are addressed while offering actionable, concrete examples for long-term maintainability.

Key Features

Analyzes error handling strategies and identifies missing edge case coverage.
Enforces idiomatic Python best practices and adherence to DRY principles.
10 GitHub stars
Identifies logic errors, performance pitfalls, and resource leaks in Python code.
Evaluates code architecture, clarity, and contract explicitness.
Provides structured review summaries with prioritized action items and fixes.

Use Cases

Reviewing complex refactors to ensure no regressions in maintainability or performance.
Auditing large pull requests or new feature implementations for production readiness.
Identifying technical debt and architectural flaws in legacy Python scripts.

About

Key Features

Analyzes error handling strategies and identifies missing edge case coverage.
Enforces idiomatic Python best practices and adherence to DRY principles.
10 GitHub stars
Identifies logic errors, performance pitfalls, and resource leaks in Python code.
Evaluates code architecture, clarity, and contract explicitness.
Provides structured review summaries with prioritized action items and fixes.

Use Cases

Reviewing complex refactors to ensure no regressions in maintainability or performance.
Auditing large pull requests or new feature implementations for production readiness.
Identifying technical debt and architectural flaws in legacy Python scripts.