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quality

Review code commits for architectural quality, complexity, and maintainability.

Updated Jun 21, 2026
Search Tags:#code quality#architecture#code review#maintainability#complexity analysis
One-click install
npx skills add https://github.com/apexradius/agentic-os --skill quality-apexradius
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: quality
Source: https://github.com/apexradius/agentic-os/tree/main/skills/quality
Command: npx skills add https://github.com/apexradius/agentic-os --skill quality-apexradius

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The quality Skill addresses the challenge of ensuring that code is not only functional but also well-architected and maintainable. It helps identify structural issues, adherence to best practices, and potential pitfalls that might not be caught by standard testing or linting.

Core Features & Use Cases

  • Architecture Review: Inspects code for adherence to single responsibility, dependency direction, and abstraction levels.
  • Code Patterns Analysis: Checks for consistency, error handling, naming conventions, and DRY principles.
  • Complexity Assessment: Identifies functions too long, high cyclomatic complexity, God objects, and dead code.
  • Maintainability Check: Ensures the code is understandable by new developers, handles edge cases, and minimizes magic numbers.
  • AI Failure Modes: Reviews for common AI-written code issues such as wrong assumptions, overcomplexity, orthogonal edits, and imperative over declarative coding.
  • Use Case: When reviewing code changes, particularly when AI-generated, the Skill can help uncover hidden issues that would otherwise go unnoticed.

Quick Start

Run a quality review on your latest code commit.

Frequently Asked Questions about quality

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

FAQPage Schema
How do I review code architecture and maintainability effectively?▼

To review code architecture and maintainability, analyze adherence to single responsibility, dependency direction, complexity, and DRY principles. This identifies structural issues and potential pitfalls missed by standard testing.

What are common AI-generated code failure modes to look for in a code review?▼

Common AI-generated code failure modes include wrong assumptions, overcomplexity, orthogonal edits, and imperative over declarative coding. Identifying these hidden issues prevents structural flaws from going unnoticed during changes.

How do I check cyclomatic complexity and identify dead code in a codebase?▼

To check cyclomatic complexity and identify dead code, assess functions for excessive length and look for God objects. This complexity assessment highlights unmaintainable structures and isolates redundant logic within the codebase.

Does this code review approach catch issues that standard linting and testing miss?▼

Yes, this code review approach catches issues that standard linting and testing miss by inspecting abstraction levels, edge case handling, and magic numbers. It ensures code remains understandable by new developers.

How do I run a quality review on a specific code commit?▼

To run a quality review on a code commit, invoke the analysis with the specific commit or codebase provided. The review then inspects architectural quality, code structure, and maintainability automatically.

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