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Technical Debt Assessment

  • 460 installs
  • 305 repo stars
  • Updated March 4, 2026
  • aj-geddes/useful-ai-prompts

technical-debt-assessment is a Claude Code skill that inventories legacy hotspots, scores maintenance risk, and ranks refactors using code analysis, metrics, and impact modeling.

About

technical-debt-assessment is a Claude Code skill in aj-geddes/useful-ai-prompts that systematically identifies, measures, and prioritizes technical debt across code, architecture, tests, documentation, security, and performance categories. The workflow scans repositories for anti-patterns such as duplicated code, long methods, missing tests, outdated dependencies, and architectural coupling, then estimates remediation effort, debt hours, and ongoing interest cost if left unfixed. A priority formula weights severity, impact, interest, and effort to produce ranked remediation lists, category breakdowns, and sprint-ready summaries for refactoring, backlog grooming, acquisition due diligence, or quality gate planning. Developers reach for technical-debt-assessment when releases slow, defect rates climb, or on-call burden rises because legacy shortcuts compound. The skill includes a TechnicalDebtAssessment registry pattern for adding debt items with severity, effort, impact, and interest fields, supporting ROI-focused conversations with engineering leadership.

  • Structured debt inventory prompts
  • Risk and blast-radius scoring
  • Refactor prioritization frameworks
  • Stakeholder-ready remediation summaries
  • Regression-risk triage guidance

Technical Debt Assessment by the numbers

  • 460 all-time installs (skills.sh)
  • Ranked #246 of 1,352 Code Review & Quality skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill technical-debt-assessment

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Listed on Skillselion
Installs460
repo stars305
Last updatedMarch 4, 2026
Repositoryaj-geddes/useful-ai-prompts

How do you prioritize technical debt in a codebase?

Inventory legacy hotspots, score maintenance risk, and rank refactors so teams fix the debt most likely to slow releases, raise defect rates, or inflate on-call burden.

Who is it for?

Tech leads and senior developers planning refactors, sprint debt budgets, or due diligence on legacy repositories with measurable risk scoring.

Skip if: Greenfield projects with little legacy code or teams seeking feature design prompts instead of codebase quality analysis.

When should I use this skill?

User asks to assess technical debt, prioritize refactors, score legacy risk, or plan sprint debt remediation with effort and impact metrics.

What you get

Prioritized debt register with severity scores, remediation effort hours, interest costs, category breakdowns, and top refactor recommendations.

  • Prioritized debt register
  • Category breakdown report
  • Sprint remediation recommendations

By the numbers

  • Covers 6 technical debt categories in assessment output
  • Uses severity, effort, impact, and interest fields per debt registry item

Files

SKILL.mdMarkdownGitHub ↗

Technical Debt Assessment

Table of Contents

Overview

Systematically identify, measure, and manage technical debt to make informed decisions about code quality investments.

When to Use

  • Legacy code evaluation
  • Refactoring prioritization
  • Sprint planning
  • Code quality initiatives
  • Acquisition due diligence
  • Architectural decisions

Quick Start

Minimal working example:

interface DebtItem {
  id: string;
  title: string;
  description: string;
  category: "code" | "architecture" | "test" | "documentation" | "security";
  severity: "low" | "medium" | "high" | "critical";
  effort: number; // hours
  impact: number; // 1-10 scale
  interest: number; // cost per sprint if not fixed
}

class TechnicalDebtAssessment {
  private items: DebtItem[] = [];

  addDebtItem(item: DebtItem): void {
    this.items.push(item);
  }

  calculatePriority(item: DebtItem): number {
    const severityWeight = {
      low: 1,
      medium: 2,
      high: 3,
      critical: 4,
    };
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

GuideContents
Technical Debt CalculatorTechnical Debt Calculator
Code Quality ScannerCode Quality Scanner

Best Practices

✅ DO

  • Quantify debt impact
  • Prioritize by ROI
  • Track debt over time
  • Include debt in sprints
  • Document debt decisions
  • Set quality gates

❌ DON'T

  • Ignore technical debt
  • Fix everything at once
  • Skip impact analysis
  • Make emotional decisions

Related skills

How it compares

Pick technical-debt-assessment over generic code review skills when you need quantified debt scoring, interest modeling, and prioritized refactor lists across multiple quality dimensions.

FAQ

What categories does technical-debt-assessment cover?

technical-debt-assessment covers six debt categories—code, architecture, test, documentation, security, and performance—scanning each for anti-patterns, estimated remediation effort, and ongoing interest if unresolved.

How does technical-debt-assessment rank debt items?

technical-debt-assessment ranks debt items with a priority formula that weights severity, business impact, ongoing interest cost, and estimated remediation effort, producing ordered lists for refactoring and sprint planning.

When should teams run technical-debt-assessment?

Teams should run technical-debt-assessment before major releases or migrations, during backlog grooming, for acquisition due diligence, or when defect rates and on-call load suggest legacy debt is slowing delivery.

Code Review & Qualitybackendfrontendtesting

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