name: code-quality-review
description: Conducts comprehensive code quality reviews including code smells detection, maintainability assessment, complexity analysis, design pattern evaluation, naming conventions, code duplication, technical debt identification, and best practices validation. Produces detailed review reports with specific issues, severity ratings, metrics analysis, and actionable improvement recommendations. Use when reviewing code quality, analyzing code maintainability, detecting code smells, checking coding standards, measuring code complexity, identifying technical debt, or when users mention "code quality review", "code quality check", "maintainability analysis", "code smells", "clean code", "refactoring candidates", or "technical debt assessment".

Code Quality Review

Overview

Conducts systematic code quality analysis across multiple dimensions: maintainability, readability, complexity, design patterns, naming conventions, code duplication, and adherence to best practices. Produces actionable feedback with severity ratings and specific improvement recommendations.

Core Capabilities

1. Code Smells Detection - Identifies bloaters, object-orientation abusers, change preventers, dispensables, and couplers
2. Complexity Analysis - Measures cyclomatic and cognitive complexity with risk assessment
3. Maintainability Assessment - Evaluates code maintainability index and technical debt
4. Design Pattern Evaluation - Reviews architectural patterns and SOLID principles
5. Best Practices Validation - Checks adherence to language-specific standards and conventions

Review Workflow

Step 1: Scope Assessment

Determine review scope based on change size:

• Small (<100 lines): Quick correctness check, 15-30 minutes
• Medium (100-500 lines): Full quality analysis, 1-2 hours
• Large (>500 lines): Architectural review, break into smaller reviews if possible, 2-4 hours

For scope-specific guidance, see review-scope-guidelines.md

Step 2: Initial Assessment

Gather Context:

• Identify programming language and framework
• Understand project type (web app, API, library, CLI, etc.)
• Note existing coding standards or style guides
• Check for linter configuration files (.eslintrc, .pylintrc, checkstyle.xml, etc.)

Read the Code:

• Start with entry points (main files, index files)
• Review module/package organization
• Check dependency management
• Examine test files if available

Step 3: Quality Analysis

Analyze code across key dimensions:

• Code Smells: Long methods, large classes, duplicate code, dead code, etc.
• Complexity: Cyclomatic complexity (target <15), cognitive complexity, nesting depth
• Maintainability: Clear naming, proper abstraction, separation of concerns
• Design Patterns: Appropriate pattern usage, SOLID principles adherence
• Best Practices: Language idioms, error handling, resource management

For detailed analysis criteria and thresholds, see review-workflow.md

For quality metrics and thresholds, see quality-metrics-reference.md

Step 4: Document Findings

Structure the review report with:

• Executive summary with scores and top priorities
• Detailed findings with severity, location, description, and recommendations
• Metrics summary with current vs. target values
• Prioritized recommendations (P0-P3)
• Positive observations acknowledging good practices
• Technical debt summary with effort estimates

For complete report structure and output guidelines, see review-report-format.md

Quality Assurance

Use the checklist to ensure comprehensive reviews:

• Code organization and structure
• Naming conventions and clarity
• Complexity thresholds
• Error handling patterns
• Testing and documentation
• Security considerations
• Performance implications

For complete checklist, see best-practices-checklist.md

Common Pitfalls

Avoid these common review mistakes:

• Focusing only on style issues instead of substantive problems
• Being overly critical without actionable suggestions
• Ignoring context and business constraints
• Overwhelming with too many issues at once
• Using vague terms without explanation
• Forgetting to acknowledge good practices

For detailed guidance, see common-pitfalls-to-avoid.md

Example Patterns

For reference when identifying critical issues in your review, see examples of common high-severity problems in critical-issues.md