SKILL.md
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:
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