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FrancisVarga
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tech-debt

Scan codebases for debt indicators and prioritize repayment in a register.

Updated May 13, 2026
Search Tags:#code quality#technical debt#prioritization#sprint planning#duplication detection#codebase scanning#documentation register
One-click install
npx skills add https://github.com/FrancisVarga/the-dream-machine --skill tech-debt-francisvarga
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tech-debt
Source: https://github.com/FrancisVarga/the-dream-machine/tree/main/.claude/skills/tech-debt
Command: npx skills add https://github.com/FrancisVarga/the-dream-machine --skill tech-debt-francisvarga

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you identify, organize, and prioritize technical debt so it doesn’t quietly accumulate into costly complexity and quality regressions.

Core Features & Use Cases

  • Scan for debt signals: Finds common debt indicators like TODO/FIXME/HACK markers, deprecated annotations, duplicated code patterns, and oversized files/functions; then categorizes the findings.
  • Maintain a debt register: Supports adding new debt entries and tracking them over sprints in a structured docs/tech-debt-register.md format.
  • Prioritize and report trends: Re-scores items using impact, frequency, and fix effort to recommend sprint scope; generates a summary report with category counts and aging flags.

Real-world use case: At the start of each sprint, scan for new debt, append a few newly discovered items, then prioritize the highest ROI fixes and produce a short report for the team.

Quick Start

Run the tech-debt scan to identify debt indicators across the codebase and propose writing the results to docs/tech-debt-register.md.

Frequently Asked Questions about tech-debt

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

FAQPage Schema
How do I track technical debt across sprints?▼

Track technical debt by scanning your codebase for indicators like TODO and FIXME markers, recording findings in a structured register, and prioritizing repayment tasks for each sprint based on impact and effort.

What is the best way to scan a codebase for technical debt indicators?▼

Scanning for technical debt involves detecting TODO/FIXME/HACK markers, deprecated annotations, duplicated code patterns, and oversized files, then categorizing these findings to assess code quality and complexity hotspots.

How do I prioritize technical debt repayment for sprint planning?▼

Prioritize technical debt repayment by re-scoring logged items using impact, frequency, and fix effort to recommend sprint scope, generating a summary report that highlights the highest ROI fixes for the team.

Can I detect code duplication and complexity hotspots automatically?▼

Yes, automated codebase scanning can detect duplicated code patterns and oversized files or functions, categorizing them alongside deprecated usages to map out complexity hotspots for a technical debt register.

Does this technical debt tracking approach require external dependencies?▼

No, tracking technical debt uses read and write operations on a local markdown register without unsafe external data access, requiring only subcommand-driven execution for scanning, adding, prioritizing, and reporting debt.

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