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code-audit

Automate code review for security, correctness, performance, and architecture.

1|Updated May 19, 2026
Search Tags:#quality assurance#code review#security audit#performance analysis#architecture review
One-click install
npx skills add https://github.com/victormacaubas/data-engineering-skills --skill code-audit-victormacaubas
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-audit
Source: https://github.com/victormacaubas/data-engineering-skills/tree/main/skills/code-audit
Command: npx skills add https://github.com/victormacaubas/data-engineering-skills --skill code-audit-victormacaubas

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pypdf, pdfplumber, pdf2image, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the code review process, identifying potential issues in code before deployment, saving time and reducing risk.

Core Features & Use Cases

  • Security Analysis: Detects vulnerabilities like hardcoded secrets, injection flaws, and over-broad permissions.
  • Correctness Checks: Identifies hidden bugs, such as off-by-one errors, null/empty/absent/zero handling, and silent divergence from documentation.
  • Performance Evaluation: Flags performance issues like N+1 queries, redundant computations, and unbounded reads.
  • Architecture Review: Evaluates modularity, separation of concerns, and adherence to design patterns.
  • Use Case: Use the skill to perform a code review on a PR, branch, or entire repository to ensure code quality and security.

Quick Start

Run the code-audit skill on your Python code located at 'path/to/your/repository'.

Frequently Asked Questions about code-audit

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

FAQPage Schema
How do I automate code review for security vulnerabilities and performance issues?▼

Automated code review scans codebases for security vulnerabilities, correctness, performance, and architectural quality. It identifies injection flaws, null pointer exceptions, off-by-one errors, and inefficient algorithms to ensure code quality before deployment.

Does automated code review support Python, JavaScript, and SQL codebases?▼

Automated code review supports Python, JavaScript, TypeScript, SQL, and Terraform codebases. Language-specific packs are required to analyze code written in these languages for security, correctness, and architecture issues.

What types of security flaws and bugs can an automated code audit detect?▼

An automated code audit detects hardcoded secrets, injection flaws, over-broad permissions, off-by-one errors, null handling issues, silent divergence from documentation, N+1 queries, and redundant computations.

How do I run a code audit on a pull request or an entire repository?▼

Run the code audit skill on your Python code located at your repository path. The skill evaluates modularity, separation of concerns, and adherence to design patterns across a PR, branch, or entire repository.

Do I need language-specific packs to perform a security and architecture review?▼

Language-specific packs are required to perform security and architecture reviews. The code audit skill uses these packs to scan for common issues like injection flaws, null pointer exceptions, and inefficient algorithms.

What are the limitations of automated code quality analysis for Terraform and Python?▼

Automated code quality analysis requires language-specific packs for Python, JavaScript, TypeScript, SQL, and Terraform. It focuses on security vulnerabilities, correctness, performance, and architecture but does not replace comprehensive manual code review.

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