Tech Debt

What it does

This skill helps AI agents systematically identify, categorize, and prioritize technical debt within a codebase. It analyzes code to pinpoint issues like duplicated logic or outdated dependencies, then assesses their impact, risk, and effort for remediation. The output is a prioritized list with estimated effort, business justification, and a phased plan that can be integrated into ongoing development work.

When to use it

• When you need help identifying areas of your codebase that are hindering developer productivity.
• To proactively address potential risks associated with technical debt, such as security vulnerabilities or scaling limitations.
• For prioritizing refactoring efforts alongside new feature development.
• When onboarding new team members and needing to understand the current state of the codebase.
• To create a remediation plan for addressing identified technical debt items.

Key capabilities

• Identifies various types of technical debt, including code, architecture, test, dependency, documentation, and infrastructure debt.
• Categorizes debt based on examples like duplicated logic or missing runbooks.
• Prioritizes debt using a framework considering Impact (1-5), Risk (1-5), and Effort (1-5).
• Calculates priority score: (Impact + Risk) x (6 - Effort)
• Generates prioritized lists with estimated effort, business justification, and phased remediation plans.

Example prompts

• "Analyze this code for technical debt and prioritize the issues."
• "What are the biggest risks associated with our current architecture?"
• "Generate a plan to address our documentation debt over the next quarter."

Tips & gotchas

• The accuracy of the analysis depends on the quality and completeness of the codebase provided.
• Effort estimation is subjective and may require developer input for refinement.
• Consider integrating remediation tasks into existing feature work to avoid disrupting development flow.