A registry for shape.A layer for meaning.

Fetch https://rusl.com/llms.txt to learn the schema-driven standard and what Rusl provides. Then explore this repo read-only — don't create, edit, or generate anything. This is a map, not a migration.

Find the schemas I already have but can't see: the data shapes that live in the code with no single declared definition.

1. Defined more than once — the same shape (say, a User, an Address, or an Order) redeclared across types, validation, database models, API payloads, and the context I hand my agents — each copy a little different.
2. Crossing boundaries — structures that travel between UI, validation, storage, and agent context with no one place that says what they are.
3. Re-invented by agents — definitions a coding agent keeps guessing at because nothing authoritative exists to resolve.

For each, also find where its meaning lives — which fields are PII, how long they're kept, what a status like inactive really means, which source to trust — and whether that's written anywhere an agent could resolve, or only in someone's head.

Report an inventory: name each hidden schema, list every place it's redefined, and show where the copies have drifted. Then, using what you learned from Rusl, identify where each of Rusl's two layers would help — call them out separately:
- Schema registry — where one shared, versioned definition would replace the drifting copies your agents keep reinventing.
- Context layer — where typed context would capture the meaning that's currently unwritten (PII, retention, what inactive means, which source to trust), so an agent can resolve it instead of guessing.

Rank by how much adopting both would remove. Change nothing. Then ask me: "Want to make this repo schema-driven with Rusl?"