What is the technical debt quantifier?

A capability to measure, categorize, and prioritize debt to support migration planning and remediation strategies.

How are interest and ROI calculated?

Interest models ongoing maintenance costs and productivity impact, projects future debt growth, and ROI balances remediation cost against business impact and gains.

Which tools integrate with this skill?

SonarQube, CodeScene, Codacy, Code Climate, and NDepend via API/CLI for debt calculation and metrics.

technical-debt-quantifier

npx machina-cli add skill a5c-ai/babysitter/technical-debt-quantifier --openclaw

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SKILL.md

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Technical Debt Quantifier Skill

Measures, categorizes, and prioritizes technical debt to support informed decision-making for migration planning and debt remediation strategies.

Purpose

Enable technical debt management for:

Debt categorization and inventory
Remediation effort estimation
Interest calculation (ongoing cost)
Priority scoring
Trend tracking

Capabilities

1. Debt Categorization

Code debt (smells, complexity)
Architecture debt (coupling, cohesion)
Test debt (coverage gaps)
Documentation debt
Infrastructure debt

2. Remediation Effort Estimation

Estimate fix time per item
Calculate total remediation cost
Identify quick wins
Plan sprint allocation

3. Interest Calculation

Calculate ongoing maintenance cost
Estimate productivity impact
Project future debt growth
Model compound interest

4. Priority Scoring

Score by business impact
Weight by risk level
Factor in remediation cost
Calculate ROI of fixes

5. Debt-to-Value Ratio

Compare debt to feature velocity
Benchmark against industry
Track debt percentage
Set organizational targets

6. Trend Tracking

Monitor debt over time
Track remediation progress
Identify debt sources
Report debt velocity

Tool Integrations

Tool	Purpose	Integration Method
SonarQube	Debt calculation	API
CodeScene	Hotspot analysis	API
Codacy	Quality metrics	API
Code Climate	Maintainability	API
NDepend	.NET debt analysis	CLI

Output Schema

{
  "analysisId": "string",
  "timestamp": "ISO8601",
  "debt": {
    "total": {
      "estimatedHours": "number",
      "monetaryValue": "number",
      "items": "number"
    },
    "byCategory": {
      "code": {},
      "architecture": {},
      "test": {},
      "documentation": {}
    },
    "byPriority": {
      "critical": [],
      "high": [],
      "medium": [],
      "low": []
    }
  },
  "metrics": {
    "debtRatio": "number",
    "debtPerLoc": "number",
    "interestRate": "number"
  },
  "trends": {
    "thirtyDay": "number",
    "ninetyDay": "number"
  },
  "recommendations": []
}

Integration with Migration Processes

legacy-codebase-assessment: Debt quantification
technical-debt-remediation: Prioritization

Related Skills

code-smell-detector: Debt identification
static-code-analyzer: Quality metrics

Related Agents

technical-debt-auditor: Deep debt analysis

Source

git clone https://github.com/a5c-ai/babysitter/blob/main/plugins/babysitter/skills/babysit/process/specializations/code-migration-modernization/skills/technical-debt-quantifier/SKILL.md

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Overview

Measures, categorizes, and prioritizes technical debt to support migration planning and remediation strategies. It inventories debt across code, architecture, tests, documentation, and infrastructure, estimates remediation effort, models ongoing costs, and scores priorities to guide sprint planning and budgeting.

How This Skill Works

It collects signals from code quality tools, classifies debt into categories (code, architecture, test, documentation, infrastructure), estimates fix time and cost, and calculates ongoing interest and ROI. It outputs a structured debt snapshot: total, byCategory, byPriority, metrics, and trends to inform migration decisions.

When to Use It

During migration planning to quantify scope and sequencing
Before remediation sprints to identify quick wins and allocate effort
For trend tracking to monitor debt velocity over time
When benchmarking debt against industry targets
To estimate ROI and justify remediation budget

Quick Start

Step 1: Identify debt categories (code, architecture, test, documentation, infrastructure) and select integrated tools
Step 2: Run measurements to produce total debt, byCategory, and byPriority using SonarQube, CodeScene, Codacy, Code Climate, and NDepend
Step 3: Generate an initial report and embed it into the migration plan, then monitor trends and ROI

Best Practices

Define debt categories upfront: code, architecture, test, documentation, infrastructure
Automate data collection with SonarQube, CodeScene, Codacy, Code Climate, NDepend
Estimate time and cost per item; identify quick wins
Calculate debt-to-value ratio and track against feature velocity
Review and adjust priorities with stakeholders on a regular cadence

Example Use Cases

Legacy monolith migration: quantify architecture and code debt to plan the migration backlog
App with patchy test coverage: quantify test debt to guide test suite expansion
Maintenance-heavy product: model ongoing interest to justify refactoring budget
Industry benchmarking: compare debt percentage against targets and set goals
Debt velocity monitoring: track remediation progress to adapt the plan

Frequently Asked Questions

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