An AI-legible repository makes its structure and intent easy for an assistant to discover. It has clear names, small focused modules, typed interfaces, current documentation, reliable tests, and standard commands. Important behavior is written down instead of living only in team memory.
This also helps human developers. An agent can find the owning module, inspect a nearby example, run one documented test command, and understand what success means. Hidden setup, duplicate patterns, vague names, and stale docs force it to guess. Improve legibility by making boundaries explicit, removing dead code, documenting unusual decisions, and keeping examples current. The goal is not to write for a model alone; it is to make project knowledge visible and verifiable.
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