Analyze code complexity, cyclomatic complexity, maintainability index, and code churn using metrics tools. Use when assessing code quality, identifying refactoring candidates, or monitoring technical debt.

Code Metrics Analysis Table of Contents [Overview]( overview) [When to Use]( when to use) [Quick Start]( quick start) [Reference Guides]( reference guides) [Best Practices]( best practices) Overview Measure and analyze code quality metrics to identify complexity, maintainability issues, and areas for improvement. When to Use Code quality assessment Identifying refactoring candidates Technical debt monitoring Code review automation CI/CD quality gates Team performance tracking Legacy code analysis Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents [TypeScript Complexity Analyzer](references/typescript complexity analyzer.md) TypeScript Complexity Analyzer [Python Code Metrics (using radon)](references/python code metrics using radon.md) Python Code Metrics (using radon) [ESLint Plugin for Complexity](references/eslint plugin for complexity.md) ESLint Plugin for Complexity [CI/CD Quality Gates](references/cicd quality gates.md) CI/CD Quality Gates Best Practices ✅ DO Monitor metrics over time Set reasonable thresholds Focus on trends, not absolute numbers Automate metric collection Use metrics to guide refactoring Combine multiple metrics Include metrics in code reviews ❌ DON'T Use metrics as sole quality indicator Set unrealistic thresholds Ignore context and domain Punish developers for metrics Focus only on one metric Skip documentation

code-metrics-analysis