GEAR

in the “Taming AI Coding Agents with TDD” series. The first covered test-driven requirements anchoring, the second covered the GEAR protocol for requirements disambiguation. This one fills the gap between them: after the PRD is done, what must the tech spec cover? Requirements Locked, Code Still Wrong Before the second Aristotle refactor, I spent two full days writing requirements. Following the structured approach from the previous article, I captured every acceptance criterion, boundary condition, error path, and platform constraint[1]. The AI consumed the document, passed all 37 static assertions plus end-to-end tests. The codebase was split into four files by responsibility. Information flow was switched from push to pull. ...

in the “Taming AI Coding Agents with TDD” series. The first article covered requirement anchoring at the test layer[1]. Tests assume clear requirements. This one goes upstream — to the practice of disambiguating requirements before a single line of code gets written. The v1 Lesson: One-Line Requirement, 371 Lines of Pollution Aristotle v1 had no GEAR protocol[2]. No role separation. The entire reflection feature lived in a single 371-line SKILL.md. The requirement was roughly one sentence: the system should detect when a user corrects an AI mistake, then generate a reusable rule. ...

The previous article, From Scars to Armor: Harness Engineering in Practice, ended with Aristotle having a streamlined router (SKILL.md compressed from 371 lines to 84), an on-demand progressive disclosure architecture, and a working reflect→review→confirm workflow. But one thread never got pulled: Where do confirmed rules actually live? This article follows that thread. It wasn’t planned from the start. Three concrete problems in actual use forced the design out, step by step. ...