technical-debt-detector

Identify and prioritize technical debt in Python projects with structured reports.

Updated Aug 23, 2026

Search Tags:#security #python #maintainability #dependencies #analysis #reporting #technical-debt

Ready to run

npx skills add https://github.com/charlesmsiegel/claude-tooling --skill technical-debt-detector

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: technical-debt-detector
Source: https://github.com/charlesmsiegel/claude-tooling/tree/main/skills/technical-debt-detector
Command: npx skills add https://github.com/charlesmsiegel/claude-tooling --skill technical-debt-detector

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Identify and prioritize technical debt in Python projects, helping teams focus refactoring where it matters and reduce risk.

Core Features & Use Cases

Multi-faceted analyses: deferred work, security issues, test coverage gaps, maintainability, and dependency health.
Prioritized reports with high-impact items and actionable fix sketches.
Use cases: onboarding codebases, sprint planning, and codebase risk audits.

Quick Start

Run the master analysis to scan a Python project and emit a prioritized debt report.

Frequently Asked Questions about technical-debt-detector

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

FAQPage Schema

How do I identify and prioritize technical debt in a large Python codebase?▼

To identify technical debt in Python projects, run an analysis that scans for deferred work, security issues, testing gaps, maintainability concerns, and outdated dependencies. The tool aggregates findings by severity and outputs a structured text or JSON report for prioritized refactoring.

What is technical debt analysis and how does it help with sprint planning?▼

Technical debt analysis systematically surfaces deferred work, security vulnerabilities, and testing gaps across repositories. It helps sprint planning by generating prioritized, actionable fix sketches, allowing teams to focus refactoring efforts on high-impact areas and reduce codebase risk.

Can I integrate Python technical debt detection into my CI pipeline?▼

Yes, this technical debt detection supports integration across repositories and CI pipelines. It applies modular analyzers to scan Python projects continuously, outputting structured JSON or text reports that highlight maintainability concerns and dependency health during automated builds.

Does Python technical debt analysis check for outdated dependencies and security issues?▼

Yes, Python technical debt analysis checks dependency health and security issues using dedicated modular analyzers. It scans your project to detect outdated dependencies, security vulnerabilities, and testing gaps, aggregating these findings into a prioritized, structured report.

How do I get a structured JSON report of codebase risk and maintainability issues?▼

You get a structured JSON report of codebase risk by running the master analysis on your Python project. The process evaluates maintainability, security, and dependency health, aggregating the findings with severity levels to output actionable data in either text or JSON format.