About principle-model-the-domain
A design skill for developers that recommends representing domain rules in data structures instead of spreading them across conditionals. It helps reduce branching and invalid states while cautioning against abstractions that add indirection without simplifying code.
Key features
- 01Represent domain rules in structures
- 02Use state machines and typed models
- 03Centralize repeated behavior and invariants
- 04Avoid needless abstraction
Use cases
- 01Replacing scattered lifecycle flags with a state model
- 02Consolidating branching rules in a registry or lookup table
- 03Choosing a data structure for a domain's access patterns
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/principle-model-the-domainin Claude Code to confirm it loaded.
Bundled filesLICENSE
Details
From the project READMEModel the Domain
Encode the real domain in a data structure instead of scattering it across conditionals.
Why: Scattered booleans, repeated shape assumptions, and branching spread across files are accidental complexity. A structure that matches the domain makes invalid states unrepresentable and deletes branches. Choosing it at write time is cheap. Recovering it later reads as a refactor and gets deferred.
Reach for structures like these:
- A state machine instead of scattered booleans, phases, or lifecycle checks.
- A typed object/model instead of loose parameters or repeated shape assumptions.
- A map, registry, lookup table, or discriminated union instead of branching spread across files.
- A reducer or command/event model instead of ad hoc state mutations.
- A module organized around one body of domain knowledge instead of a sequence such as load, validate, transform, and save. Execution order is not ownership.
- A small module boundary that gathers repeated behavior, ownership, or invariants.
- A queue, cache, index, graph/tree, or normalized collection where the data access pattern calls for it.
- Any other structure that fits. When none fits, work out what the code must never allow and how the data gets read, then find the structure that encodes exactly that.
Do not force an abstraction. Prefer boring code if the current shape is already clear, local, and unlikely to grow. Be skeptical of an abstraction that adds indirection without removing branches, duplicated rules, invalid states, or lifecycle risk.
The sign that you skipped this is a new feature that grows an existing if/else chain by one more branch, or a second boolean that must stay in sync with the first. Temporal decomposition is another sign. Phase-named modules repeat the same domain rules across steps.