tech-debt-tracker

# 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Tech debt isn't just about messy code - it encompasses architectural shortcuts, missing tests, outdated dependencies, documentation gaps, and infrastructure compromises.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.

## Technical Debt Classification Framework
→ See references/debt-frameworks.md for details

## Implementation Roadmap

### Phase 1: Foundation (Weeks 1-2)
1. Set up debt scanning infrastructure
2. Establish debt taxonomy and scoring criteria
3. Scan initial codebase and create baseline inventory
4. Train team on debt identification and reporting

### Phase 2: Process Integration (Weeks 3-4)
1. Integrate debt tracking into sprint planning
2. Establish debt budgets and allocation rules
3. Create stakeholder reporting templates
4. Set up automated debt scanning in CI/CD

### Phase 3: Optimization (Weeks 5-6)
1. Refine scoring algorithms based on team...

# Tech Debt Tracker

A comprehensive technical debt management system that helps engineering teams identify, prioritize, and track technical debt across codebases. This skill provides three interconnected tools for a complete debt management workflow.

## Overview

Technical debt is like financial debt - it compounds over time and reduces team velocity if not managed systematically. This skill provides:

- **Automated Debt Detection**: Scan codebases to identify various types of technical debt
- **Intelligent Prioritization**: Use proven frameworks to prioritize debt based on business impact
- **Trend Analysis**: Track debt evolution over time with executive-friendly dashboards

## Tools

### 1. Debt Scanner (`debt_scanner.py`)

Scans codebases to automatically detect technical debt signals using AST parsing for Python and regex patterns for other languages.

**Features:**
- Detects 15+ types of technical debt (large functions, complexity, duplicates, security issues, etc.)
- Multi-language support (Python, JavaScript, Java, C#, Go, etc.)
- Configurable thresholds and rules
- Dual output: JSON for tools, human-readable for reports

**Usage:**
```bash
# Basic scan
python scripts/debt_scanner.py /path/to/codebase

# With custom config and output
python scripts/debt_scanner.py /path/to/codebase --config config.json --output report.json

# Different output formats
python scripts/debt_scanner.py /path/to/codebase --format both
```

### 2. Debt Prioritizer (`debt_prioritizer.py`)

Takes debt inventory and creates prioritized backlog using proven prioritization frameworks.

**Features:**
- Multiple prioritization frameworks (Cost of Delay, WSJF, RICE)
- Business impact analysis with ROI calculations  
- Sprint allocation recommendations
- Effort estimation with risk adjustment
- Executive and engineering reports

**Usage:**
```bash
# Basic prioritization
python scripts/debt_prioritizer.py debt_inventory.json

# Custom framework and team size
python scripts/debt_prioritizer...