How is the review feedback structured?

Feedback is organized into three clear sections: Strengths (what is done well), Issues/Risks (critical logic or security flaws), and Suggestions (actionable improvements).

What specific Python standards does this skill check?

The skill primarily focuses on PEP 8 compliance, proper naming conventions, type hints, and the elimination of redundant or dead code.

Can it detect security vulnerabilities?

Yes, it identifies risky patterns such as unsafe I/O, SQL injection risks, the use of eval/exec/pickle, and potentially leaked hardcoded secrets.

Is it limited to small files?

While it can review large modules, it is designed to understand the context, architecture, and interactions of the entire module before providing comments.

Does it provide code examples for fixes?

Yes, the skill includes inline code snippets where helpful to demonstrate exactly how to implement the recommended improvements.

Python Code Review Assistant

Name: Python Code Review Assistant
Author: dmitriiweb

bydmitriiweb

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106

Herramientas de Colaboración

Conducts structured, actionable Python code reviews to improve architectural integrity, security, and PEP 8 compliance.

Acerca de

The Python Code Review Assistant is a specialized capability designed to automate and standardize the review process for Python projects. It performs deep analysis of code modules to identify logic errors, edge cases, and architectural flaws while ensuring strict adherence to PEP 8 standards. Beyond simple linting, the skill evaluates algorithmic efficiency, assesses security risks such as injection vulnerabilities or leaked secrets, and provides feedback categorized into strengths, risks, and actionable suggestions to streamline the development workflow.

Características Principales

Algorithmic efficiency and data structure optimization feedback
106 GitHub stars
Structured reporting grouped by strengths, risks, and suggestions
Automated PEP 8 compliance and naming convention validation
Security auditing for unsafe patterns like eval/exec and secret leakage
Deep logic assessment including error handling and edge case detection

Casos de Uso

Identifying hidden security vulnerabilities and technical debt in legacy codebases
Automating high-quality feedback for pull requests in Python repositories
Standardizing code quality and style across multi-developer Python teams

Acerca de

Características Principales

Algorithmic efficiency and data structure optimization feedback
106 GitHub stars
Structured reporting grouped by strengths, risks, and suggestions
Automated PEP 8 compliance and naming convention validation
Security auditing for unsafe patterns like eval/exec and secret leakage
Deep logic assessment including error handling and edge case detection

Casos de Uso

Identifying hidden security vulnerabilities and technical debt in legacy codebases
Automating high-quality feedback for pull requests in Python repositories
Standardizing code quality and style across multi-developer Python teams