domain-model-context

name	domain-model-context
description	Load a domain model into the working context before a coding task. Use at the start of a session that will touch product concepts, when the user says "use the domain model" or "load the model," or before implementing a feature in a repo that has a *.modelith.yaml. Makes the agent reason in the team's vocabulary — entity names, relationships, and invariants.

Loading the domain model as context

A well-formed domain model is a type system for the problem space. Loading it before a coding task means you use the team's exact names, respect the declared relationships, and don't violate invariants.

What to load

1. Find the domain model in the repo — a *.modelith.yaml and its rendered *.modelith.md (often under docs/ or the repo root).
2. Prefer the rendered Markdown for reading: it's the readable form, with the relationship diagram inline. If it's missing or stale (check with modelith render --check <file> — the file argument is required), regenerate it with modelith render <file> first. (If modelith isn't installed, you can still read the committed .md directly; just note it may be stale.)
3. Read it in full before writing code. Internalize:
   • the canonical names — use Project, never "workspace" or "container";
   • the relationships and cardinality — what owns what;
   • the invariants — rules your code must not break.
4. If it has an imports: list, its scope.Name references (in attribute type values) name vocabulary that lives in the imported file, not this one — load that file too before treating a scope.Name term as unknown.

How to apply it while coding

• Name variables, types, functions, and UI strings using the model's terms.
• When a requirement seems to need a concept the model doesn't have, stop: that may be a real gap. Flag it and offer to capture it with the domain-model-author skill rather than silently inventing a name.
• When code would violate an invariant, treat that as a bug in the plan, not a detail to smooth over.

Keep the model honest

If implementing the feature reveals the model is wrong or incomplete — a missing entity, an invariant that can't hold — surface it. The model is meant to be a living source of truth; coding against it is exactly when its gaps show up.