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Tech Debt Tracker

  • 587 installs
  • 23.5k repo stars
  • Updated July 17, 2026
  • alirezarezvani/claude-skills

tech-debt-tracker is a Claude skill that automatically surfaces technical debt, code smells, and security risks for developers who need structured debt inventories before refactors compound.

About

tech-debt-tracker is a Claude skill that scans project source trees and emits structured technical debt reports with severity, file paths, and health metrics. Example output shows scanner_version 1.0.0 summarizing 25 files scanned, 12,543 lines reviewed, 28 debt items found, a 68.5 health score, and 1.12 debt density across modules like user_service.py. Detected item types include large functions, duplicate code, and security-adjacent smells, each tagged with IDs such as DEBT-0001 and status fields for triage. Developers reach for tech-debt-tracker during operate and refactor cycles when they need quantified debt backlogs instead of subjective code review notes. The skill helps prioritize remediation before debt blocks feature velocity.

  • Scans 25+ files and flags 28 debt items with severity levels
  • Produces health score, debt density, and categorized findings (large_function, duplicate_code, security_risk, high_compl
  • Outputs structured JSON report with file paths, descriptions, and remediation status
  • Hard-gate: review critical and high severity items before merging
  • Next-skill handoff: feed prioritized list into refactoring workflow

Tech Debt Tracker by the numbers

  • 587 all-time installs (skills.sh)
  • Ranked #218 of 1,356 Code Review & Quality skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Jul 31, 2026 (Skillselion catalog sync)
npx skills add https://github.com/alirezarezvani/claude-skills --skill tech-debt-tracker

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Listed on Skillselion
Installs587
repo stars23.5k
Security audit3 / 3 scanners passed
Last updatedJuly 17, 2026
Repositoryalirezarezvani/claude-skills

How do you quantify technical debt in a codebase?

Automatically surface technical debt, code smells, and security risks before they compound.

Who is it for?

Tech leads running periodic debt scans who want numbered debt items, health scores, and file-level smell reports for sprint planning.

Skip if: Greenfield projects with no legacy code or teams that only need single-file lint fixes without portfolio-level debt metrics.

When should I use this skill?

The user asks to find code smells, measure tech debt density, or generate a refactor backlog with health scores from a source scan.

What you get

Debt item inventory with IDs, health score, debt density metrics, and severity-ranked code smell reports.

  • debt item JSON report
  • health score summary

By the numbers

  • Example scan reports 28 debt items across 25 files and 12,543 lines
  • Scanner version documented as 1.0.0 with health_score and debt_density metrics

Files

SKILL.mdMarkdownGitHub ↗

Tech Debt Tracker

Tier: POWERFUL 🔥 Category: Engineering Process Automation Expertise: Code Quality, Technical Debt Management, Software Engineering

Overview

Tech debt is one of the most insidious challenges in software development - it compounds over time, slowing down development velocity, increasing maintenance costs, and reducing code quality. This skill provides a comprehensive framework for identifying, analyzing, prioritizing, and tracking technical debt across codebases.

Tech debt isn't just about messy code - it encompasses architectural shortcuts, missing tests, outdated dependencies, documentation gaps, and infrastructure compromises. Like financial debt, it accrues "interest" through increased development time, higher bug rates, and reduced team velocity.

What This Skill Provides

This skill offers three interconnected tools that form a complete tech debt management system:

1. Debt Scanner - Automatically identifies tech debt signals in your codebase 2. Debt Prioritizer - Analyzes and prioritizes debt items using cost-of-delay frameworks 3. Debt Dashboard - Tracks debt trends over time and provides executive reporting

Together, these tools enable engineering teams to make data-driven decisions about tech debt, balancing new feature development with maintenance work.

Quick Start — scan → prioritize → dashboard

All paths relative to this skill folder. The scanner's JSON output feeds the prioritizer directly; dated inventory snapshots feed the dashboard.

1. Scan the codebase

python3 scripts/debt_scanner.py /path/to/codebase --format json --output debt_inventory.json

Emits debt_inventory.json with scan_metadata, summary, debt_items[], file_statistics, and recommendations. Report the summary counts to the user. (Dry run: assets/sample_codebase.)

2. Prioritize the backlog

python3 scripts/debt_prioritizer.py debt_inventory.json --framework wsjf --team-size 6 --sprint-capacity 20 --format json --output debt_priorities.json

Frameworks: cost_of_delay (default), wsjf, rice. Output contains prioritized_backlog (work top-down), sprint_allocation (paste into sprint planning), and insights.

3. Track trends over time

Keep dated snapshots (debt_YYYY-MM-DD.json), then:

python3 scripts/debt_dashboard.py --input-dir snapshots/ --period monthly --format both --output debt_dashboard

Or pass files explicitly (samples: assets/historical_debt_2024-01-15.json assets/historical_debt_2024-02-01.json). The dashboard reports trend direction and executive-ready summaries — use it to verify a cleanup sprint actually reduced debt.

Verification loop

After a remediation sprint: re-run step 1, re-run step 3 with the new snapshot, and assert the targeted categories' counts dropped. A cleanup that doesn't move the dashboard is rework, not debt paydown.

Technical Debt Classification Framework

→ See references/debt-frameworks.md for details (also: references/debt-classification-taxonomy.md, references/prioritization-framework.md, references/stakeholder-communication-templates.md)

Common Pitfalls and How to Avoid Them

1. Analysis Paralysis

Problem: Spending too much time analyzing debt instead of fixing it. Solution: Set time limits for analysis, use "good enough" scoring for most items.

2. Perfectionism

Problem: Trying to eliminate all debt instead of managing it. Solution: Focus on high-impact debt, accept that some debt is acceptable.

3. Ignoring Business Context

Problem: Prioritizing technical elegance over business value. Solution: Always tie debt work to business outcomes and customer impact.

4. Inconsistent Application

Problem: Some teams adopt practices while others ignore them. Solution: Make debt tracking part of standard development workflow.

5. Tool Over-Engineering

Problem: Building complex debt management systems that nobody uses. Solution: Start simple, iterate based on actual usage patterns.

Technical debt management is not just about writing better code - it's about creating sustainable development practices that balance short-term delivery pressure with long-term system health. Use these tools and frameworks to make informed decisions about when and how to invest in debt reduction.

Related skills

How it compares

Use tech-debt-tracker for portfolio debt inventories and health scores; use ESLint or language linters when you need rule-by-rule style enforcement on each commit.

FAQ

What metrics does tech-debt-tracker report?

tech-debt-tracker reports total files scanned, lines scanned, total debt items, health_score, and debt_density. An example scan of 25 files and 12,543 lines found 28 debt items with a 68.5 health score and 1.12 debt density.

What debt types does tech-debt-tracker detect?

tech-debt-tracker detects items such as large functions and duplicate code, assigning IDs like DEBT-0001 with severity, file_path, and status fields. Output is structured JSON suitable for sprint backlog import and prioritization.

Is Tech Debt Tracker safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

Code Review & Qualitybackendtestingintegrations

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