tech-debt-analyzer

Identify and quantify technical debt across multi-language codebases.

438|112|Updated Nov 6, 2025
One-click install
npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill tech-debt-analyzer-ailabs-393
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tech-debt-analyzer
Source: https://github.com/ailabs-393/ai-labs-claude-skills/tree/main/dist/skills/tech-debt-analyzer
Command: npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill tech-debt-analyzer-ailabs-393

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? This Skill systematically identifies, analyzes, documents, and tracks technical debt in JavaScript/TypeScript codebases, eliminating the manual effort and guesswork involved in maintaining code quality and long-term project health. It helps prevent future issues by integrating into your development workflow.

Core Features & Use Cases:

  • Automated Code Smell Detection: Automatically identify large files, complex functions, debt markers (TODO/FIXME), weak typing, and deep nesting.
  • Dependency Analysis: Scan package.json for deprecated packages, duplicate functionality, and versioning issues.
  • Technical Debt Register: Generate and maintain a structured register of debt items, including severity, impact, and proposed solutions.
  • Architecture Decision Records (ADRs): Provide templates and guidance for documenting major technical decisions to prevent future debt.
  • Use Case: Run a weekly scan on your project's src directory to automatically generate a report on new code smells and outdated dependencies, keeping your codebase clean.

Quick Start: Analyze the 'src' directory of my JavaScript codebase for technical debt indicators and generate a markdown report.

Frequently Asked Questions about tech-debt-analyzer

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

FAQPage Schema
How do I identify technical debt in my JavaScript codebase?

Technical debt identification scans your codebase for code smells, complex functions, deprecated dependencies, and debt markers like TODO/FIXME comments. This Skill automates that detection across your src directory and generates a structured report ranking issues by severity and remediation priority.

Can I analyze technical debt in monorepos and CI/CD pipelines?

Yes. This Skill applies to multi-language repositories, monorepos, and CI/CD pipelines. It surfaces debt types, hotspots, and remediation priorities across your entire codebase architecture, producing a debt scorecard with actionable recommendations.

What code quality issues does automated code smell detection catch?

Automated code smell detection identifies large files, deeply nested code, complex functions, weak typing patterns, and scattered debt markers. It also scans package.json for deprecated packages, duplicate functionality, and versioning issues that accumulate technical debt.

How do I prevent future technical debt using Architecture Decision Records?

ADR templates and guidance help you document major technical decisions systematically. Recording these decisions prevents future debt by making design rationale explicit and available to your team during future development.

What output does a technical debt analysis produce?

Analysis produces a structured debt register with severity and impact ratings, a prioritized remediation task list, actionable recommendations from static analysis, and dependency graphs showing hotspots and historical changes across your codebase.

Does this work with TypeScript projects?

Yes. This Skill analyzes JavaScript and TypeScript codebases, detecting weak typing patterns and code quality issues specific to both languages within your repository structure.