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wardawgmalvicious
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code-review

Automate code review for Python, PySpark, SQL, KQL, and DAX.

2|1|Updated May 5, 2026
Search Tags:#code quality#static analysis#code review#maintainability#security auditing
One-click install
npx skills add https://github.com/wardawgmalvicious/claude-config --skill code-review-wardawgmalvicious
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-review
Source: https://github.com/wardawgmalvicious/claude-config/tree/main/skills/code-review
Command: npx skills add https://github.com/wardawgmalvicious/claude-config --skill code-review-wardawgmalvicious

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the process of reviewing code for quality, safety, and maintainability, saving developers time and ensuring code quality.

Core Features & Use Cases

  • Automated Code Review: Analyze code for correctness, naming conventions, style, error handling, security, and scaling concerns.
  • Real-time Feedback: Get immediate feedback on issues and suggestions for improvement.
  • Multiple Languages: Supports Python, PySpark, SQL, KQL, DAX, and data-engineering patterns.
  • Use Case: Use the skill to review a pull request in a repository and get a detailed report on potential issues.

Quick Start

Run the /code-review command followed by the file path or branch name you want to review.

Frequently Asked Questions about code-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate code review for Python and PySpark data pipelines?▼

Automate Python and PySpark code review by running the /code-review command against specific file paths or branch names to identify correctness, security, and scaling issues. This generates a detailed report on potential maintainability and safety concerns.

Can I run static analysis on SQL and KQL queries for security auditing?▼

Static analysis supports SQL and KQL queries to identify security vulnerabilities and naming convention issues. Provide the specific file paths containing your queries to receive immediate feedback on correctness and safety improvements.

What is the best way to review a pull request for maintainability and error handling?▼

The best way to review a pull request for maintainability and error handling is using automated analysis to scan the branch for scaling concerns and style issues. This provides immediate, detailed feedback on potential code improvements.

Does automated code review work with DAX and data-engineering patterns?▼

Automated code review works with DAX and data-engineering patterns by analyzing the provided codebase for correctness and security. You must supply the specific file paths or branch names for the review process to execute successfully.

How do I check my codebase for scaling concerns and naming conventions before deployment?▼

Check your codebase for scaling concerns and naming conventions by triggering an automated review that analyzes the specified file paths. This process evaluates error handling and style to ensure code quality and maintainability before deployment.

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