Introduction

Adopting AI can feel overwhelming, so I am sharing a few tales from my experience creating, maintaining, and assisting with contributed Drupal modules. AI can do a decent job building a "greenfield" Drupal module from scratch, so my first tale is about using AI to build and contribute a module. For my next tale, I want to talk about using AI to maintain an existing "brownfield" Drupal module.

Growing up in NYC, I found brownfields often intimidating, with some so toxic that it can take years for backhoes and dump trucks to remove the contaminated topsoil before construction can begin. With code, a brownfield can contain technical debt, orphaned code, missing tests, etc. Simply put, a brownfield contains unknowns, which means more things can go wrong while working on it. Sometimes a brownfield is so big you need heavy machinery to maintain it, and that is exactly how I felt about using AI on the Schema.org Blueprints module.

Maintaining a large module using AI

Gradually, I have been experimenting with AI to help maintain the Schema.org Blueprints. At first, I would just throw a one-shot prompt at the AI to address a single issue, and it would do a decent job diagnosing and resolving the problem. As I became more confident steering the AI, I started asking it to create issue forks, commits, pushes, and even MRs. As I previously stated in my AGENTS.md I always have "Require me to review all changes before committing and pushing code." and most models respect this rule.

My comfort level reached a point where I am creating {module}-issue-maintenance agent skills that extend my drupalorg-issue-maintenance skills. These maintenance skills have agents to track remote issues in...Read More