Scrum for One: Running Sprints with an AI Coding Agent
Can the right methodology consistently produce production-quality code from an AI coding agent? Well-decomposed architecture. Maintainable code that can be refactored without disproportionate rewrites. Clean abstractions that survive the next feature. It started with one planning skill. I was tired of Claude Code implementing features freestyle, producing code that worked but couldn’t be extended. So I wrote a /grooming skill that reads the codebase and produces a structured plan before any code is written. That helped. Then I added agent delegation to avoid context rot on long sessions. Then story points for tracking throughput. Then retrospectives and lessons to stop repeating the same mistakes across sprints. Four skills, a ~/Claude/ knowledge base, and a self-learning loop that makes the agent genuinely improve across sessions. ...