code-metrics-analysis

SKILL.md

Code Metrics Analysis

Table of Contents

Overview

When to Use

Quick Start

Reference Guides

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:

import * as ts from "typescript";
import * as fs from "fs";

interface ComplexityMetrics {
  cyclomaticComplexity: number;
  cognitiveComplexity: number;
  linesOfCode: number;
  functionCount: number;
  classCount: number;
  maxNestingDepth: number;
}

class CodeMetricsAnalyzer {
  analyzeFile(filePath: string): ComplexityMetrics {
    const sourceCode = fs.readFileSync(filePath, "utf-8");
    const sourceFile = ts.createSourceFile(
      filePath,
      sourceCode,
      ts.ScriptTarget.Latest,
      true,
    );

    const metrics: ComplexityMetrics = {
      cyclomaticComplexity: 0,
      cognitiveComplexity: 0,
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide
Contents

TypeScript Complexity Analyzer
TypeScript Complexity Analyzer

Python Code Metrics (using radon)
Python Code Metrics (using radon)

ESLint Plugin for Complexity
ESLint Plugin for Complexity

CI/CD Quality Gates
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

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