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shaul1991
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debt-collector

Detect and log technical debt markers into a structured registry file.

Updated Apr 27, 2026
Search Tags:#maintainability#code-quality#static-analysis#tech-debt#pattern-detection#debt-collector#debt-registry
One-click install
npx skills add https://github.com/shaul1991/shaul-plugin --skill debt-collector
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debt-collector
Source: https://github.com/shaul1991/shaul-plugin/tree/main/claude-code-plugin/project-lifecycle/skills/debt-collector
Command: npx skills add https://github.com/shaul1991/shaul-plugin --skill debt-collector

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Detect and log technical debt markers such as TODO, FIXME, HACK, and related patterns found in a codebase, surfacing hidden debt for proactive management.

Core Features & Use Cases

  • Marker-based debt detection: identify common debt indicators like TODO, FIXME, HACK, and WORKAROUND across languages.
  • Pattern-based scanning: detect magic numbers, excessive complexity, duplicate code, and unused imports.
  • Automated registry: automatically register findings in .claude/tech-debt-registry.md with structured entries for traceability.
  • Cross-project reporting: generate scan reports and trend insights for Phase 5 implementation or QA review.

Quick Start

Run debt-collector on your repository to scan for debt markers and export findings to the tech debt registry.

Frequently Asked Questions about debt-collector

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

FAQPage Schema
How do I track technical debt markers like TODO and FIXME across a multi-language repository?▼

Technical debt markers like TODO and FIXME are tracked by scanning the repository to identify common debt indicators and logging them into a central registry file with structured entries for traceability.

What is the best way to detect magic numbers and duplicate code during a code audit?▼

Detecting magic numbers and duplicate code during a code audit is handled through pattern-based scanning, which identifies excessive complexity, duplicates, and unused imports to surface hidden technical debt.

Can I log technical debt findings automatically to a markdown registry?▼

Technical debt findings can be automatically registered to a markdown file at .claude/tech-debt-registry.md, creating structured entries that provide traceability for code audits and refactoring workflows.

How does technical debt detection apply to QA workflows and refactoring?▼

Technical debt detection applies to QA workflows and refactoring by surfacing hidden debt items such as TODOs and HACKs, generating cross-project scan reports and trend insights for review.

Does technical debt scanning work with multi-language codebases?▼

Technical debt scanning works with multi-language repositories by applying marker-based detection and pattern checks to surface debt indicators consistently across different programming languages.

What types of code patterns are checked when scanning for tech debt?▼

Code patterns checked during tech debt scanning include magic numbers, excessive complexity, duplicate code, and unused imports, alongside marker-based detection of TODO, FIXME, HACK, and WORKAROUND comments.

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