Structural Fingerprints

While lexical and comment analysis look at the text of the code, the structural detector looks at the shape of the project.

The “Cookie-Cutter” Pattern

LLMs have a very specific way of organizing projects. They tend to produce “perfect” structures:

• Perfectly balanced folder hierarchies.
• Every single file having exactly one responsibility.
• A level of consistency in naming and layout that is almost inhuman.

While this is “good” software engineering in a textbook, in the real world, human code is messier. It has “scar tissue”—remnants of previous iterations, slightly inconsistent naming in old modules, and a layout that evolved organically.

Detecting Uniformity

The structure detector analyzes:

• File Distribution: Does every module have exactly the same number of files and similar line counts?
• Boilerplate Consistency: Are the imports and headers identical across 20 different files?
• The “Day One” Project: Does the project contain a full suite of governance files (CONTRIBUTING.md, SECURITY.md) in the first few commits?

Humanizing Structure

Correcting structural fingerprints is more complex than replacing a word. The Scrubber handles this by:

1. Introducing Variance: Suggesting small reorganizations to break the “perfect” grid.
2. Removing Metadata Slop: Identifying and flagging the excessive boilerplate that screams “generated by a project initializer.”

By breaking the mathematical perfection of the LLM’s output, the project feels like it grew organically over time.