Domain Modeller

Domain Modeller

Model-first design skill. Maps entities, relationships, and boundaries before code. Prioritises understanding the domain over jumping to implementation. Outputs conceptual models as Mermaid diagrams or Cypher-compatible graph structures.

When This Skill Applies

Use this skill when:

• Designing a new feature, module, or system
• The conversation shifts from "what should we build" to "how should we build it"
• Entities and their relationships are unclear or undocumented
• Multiple data stores are involved (polyglot persistence decisions)
• A feature touches multiple parts of the system
• The user says "I'm not sure how this fits together"
• Before writing any significant new code

Core Principle

Understand the domain before solving it.

Sophistication is not clever code — it's depth of understanding. A well-modelled domain makes implementation obvious. A poorly modelled domain makes every line of code a fight.

The sequence is always: Model → Design → Build

The Modelling Process

Step 1: Identify Entities

What are the nouns? What things exist in this domain?

Questions to ask:

• What are the core objects/concepts?
• Which of these are primary (exist independently) vs secondary (exist in relation to something else)?
• What's the lifecycle of each entity?
• Are there implicit entities hiding behind properties?

Example: "Users can enrol on courses and track progress"

Entities: User, Course, Enrolment, Progress
Hidden entity: Enrolment is not just a boolean — it has state (enrolled, active, completed, dropped)

Step 2: Map Relationships

What connects to what? How? With what cardinality?

Questions to ask:

• What's the nature of each connection? (has, belongs to, creates, requires, follows)
• What's the cardinality? (one-to-one, one-to-many, many-to-many)
• Do relationships carry data? (e.g., "enrolled since", "role in team")
• Are there transitive relationships? (A→B→C implies something about A→C?)
• What's the directionality?

erDiagram
    USER ||--o{ ENROLMENT : "enrolls in"
    COURSE ||--o{ ENROLMENT : "has"
    ENROLMENT ||--|| PROGRESS : "tracks"
    COURSE ||--o{ COURSE : "requires (prerequisite)"

Step 3: Define Boundaries

Where does one concern end and another begin?

Questions to ask:

• Which entities are tightly coupled? (change together)
• Which entities are loosely coupled? (change independently)
• Where are the natural service/module boundaries?
• Which data needs strong consistency? Which can be eventually consistent?

Step 4: Choose Data Homes

Given the model, where does each entity live?

Decision framework (from data-ontologist skill):

• Relationships are primary concern → Neo4j (graph)
• Structured, transactional entities → PostgreSQL (relational)
• Flexible, nested documents → MongoDB (document)
• Simple key-value → Consider if a full database is even needed

Step 5: Output the Model

Produce a visual representation. Prefer:

1. Mermaid ER diagram — for entity-relationship overview
2. Cypher patterns — for graph-oriented domains
3. Plain text table — when the model is simple enough

Output Formats

Mermaid ER Diagram

erDiagram
    ENTITY_A ||--o{ ENTITY_B : "relationship"
    ENTITY_B }o--|| ENTITY_C : "relationship"

Cypher Pattern Map

(:User)-[:ENROLLED_IN {since: date}]->(:Course)
(:Course)-[:REQUIRES]->(:Course)
(:User)-[:COMPLETED {score: float}]->(:Module)

Entity Summary Table

Graph-First Thinking

When in doubt, start with the graph.

Most real-world domains are fundamentally about relationships. Tables and documents are storage optimisations — the graph is the truth. Start by drawing nodes and edges, then decide where to persist them.

Process:

1. Draw entities as nodes
2. Draw relationships as labelled, directed edges
3. Annotate edges with properties where relationships carry data
4. Look for patterns: chains, cycles, trees, hubs
5. Then decide which database handles which part

Common graph patterns:

• Tree: org charts, category hierarchies, file systems
• DAG (directed acyclic graph): prerequisites, build dependencies, workflows
• Social graph: follows, friendships, collaborations
• Bipartite: users↔products, students↔courses, employees↔skills

Red Flags in Domain Models

Watch for these — they usually indicate the model needs more thought:

• God entity: One entity with 20+ properties → probably multiple entities merged
• Implicit relationships: Using foreign keys where the relationship itself has meaning → model the relationship explicitly
• Missing lifecycle: Entity has no clear states → you'll fight state management later
• Circular ownership: A owns B owns C owns A → clarify the real hierarchy
• Premature normalisation: Splitting things that always change together → keep them together
• Premature denormalisation: Duplicating data that changes independently → keep it normalised

Integration Points

With data-ontologist

Domain modeller identifies what exists and how it connects. Data-ontologist decides where it lives and how to query it.

With implementation-planner

The domain model feeds directly into the implementation plan. Model first, plan second, build third.

With ethics-reviewer

Certain domain entities (user data, tracking, notifications) trigger ethical review. If the model includes personal data or behavioural tracking, flag it.

With scope-coach

A domain model often reveals more complexity than expected. Scope coach helps cut back to what's essential for the first iteration.

Anti-Patterns

Don't: Model the database schema first

✗ "We need a users table with these columns..."
✓ "Users have identities, belong to organisations, and enrol in courses..."

Don't: Skip modelling for "simple" features

✗ "It's just a CRUD form, no need to model"
✓ Even simple features benefit from 2 minutes of entity mapping

Don't: Model the entire universe

✗ Every possible future entity mapped in detail
✓ Core entities for the current scope, with notes on expansion points

Success Criteria

Domain modelling is complete when:

• All core entities are identified and named
• Relationships are explicit, directed, and labelled
• Boundaries are clear (what changes together, what's independent)
• Data homes are justified (why PostgreSQL vs Neo4j vs MongoDB)
• The model is visual (Mermaid, Cypher, or diagram)
• Hidden complexity has been surfaced
• The team could implement from the model without further questions