domain-driven-design

name	domain-driven-design
description	Model software around the business domain using bounded contexts, aggregates, and ubiquitous language. Use when the user mentions "domain modeling", "bounded context", "aggregate root", "ubiquitous language", or "anti-corruption layer". Covers entities vs value objects, domain events, and context mapping strategies. For architecture layers, see clean-architecture. For complexity, see software-design-philosophy.
license	MIT
metadata	{"author":"wondelai","version":"1.0.1"}

Domain-Driven Design Framework

Framework for tackling software complexity by modeling code around the business domain. Based on a fundamental truth: the greatest risk in software is not technical failure -- it is building a model that does not reflect how the business actually works.

Core Principle

The model is the code; the code is the model. Software should embody a deep, shared understanding of the business domain. When domain experts and developers speak the same language and that language is directly expressed in the codebase, complexity becomes manageable, requirements are captured precisely, and the system evolves gracefully as the business changes.

Scoring

Goal: 10/10. When reviewing or creating domain models, rate them 0-10 based on adherence to the principles below. A 10/10 means full alignment with all guidelines; lower scores indicate gaps to address. Always provide the current score and specific improvements needed to reach 10/10.

Framework

1. Ubiquitous Language

Core concept: A shared, rigorous language between developers and domain experts that is used consistently in conversation, documentation, and code. When the language changes, the code changes. When the code reveals awkward naming, the language is refined.

Why it works: Ambiguity is the root cause of most modeling failures. When a developer says "order" and a domain expert means "purchase request," bugs are inevitable. A ubiquitous language forces alignment so that every class, method, and variable name maps to a concept the business recognizes and validates.

Key insights:

• The language is not a glossary bolted on after the fact -- it emerges from deep collaboration
• If a concept is hard to name, the model is likely wrong; naming difficulty is a design signal
• Code that uses technical jargon instead of domain terms (e.g., DataProcessor vs. ClaimAdjudicator) hides domain logic
• Language must be enforced in code: class names, method names, event names, module names
• Different bounded contexts may use the same word with different meanings -- and that is fine

Code applications:

Context	Pattern	Example
Class naming	Name classes after domain concepts	LoanApplication, not RequestHandler
Method naming	Use verbs the business uses	policy.underwrite(), not policy.process()
Event naming	Past-tense domain actions	ClaimSubmitted, not DataSaved
Module structure	Organize by domain concept	shipping/, billing/, not controllers/, services/
Code review	Reject technical-only names	Flag Manager, Helper, Processor, Utils as naming smells

See: references/ubiquitous-language.md

2. Bounded Contexts and Context Mapping

Core concept: A bounded context is an explicit boundary within which a particular domain model is defined and applicable. The same word (e.g., "Customer") can mean different things in different contexts. Context maps define the relationships and translation strategies between bounded contexts.

Why it works: Large systems that try to maintain a single unified model inevitably collapse into inconsistency. Bounded contexts accept that different parts of the business have different models and make the boundaries explicit. Context maps then manage integration so that each context preserves its internal consistency.

Key insights:

• A bounded context is not a microservice -- it is a linguistic and model boundary that may contain multiple services
• Context boundaries often align with team boundaries (Conway's Law)
• The nine context mapping patterns describe political and technical relationships between teams
• Anti-Corruption Layer is the most important defensive pattern -- never let a foreign model leak into your core domain
• Shared Kernel is dangerous: it couples two teams and should be small and explicitly governed
• Start by mapping what exists (Big Ball of Mud), then define target boundaries

Code applications:

Context	Pattern	Example
Service integration	Anti-Corruption Layer	Translate external API responses into your domain objects at the boundary
Team collaboration	Shared Kernel	Two teams co-own a small Money value object library
Legacy migration	Conformist / ACL	Wrap legacy system behind an adapter that speaks your domain language
API design	Open Host Service + Published Language	Expose a well-documented REST API with a canonical schema
Module boundaries	Separate packages per context	myapp.shipping and myapp.billing packages with explicit translation

See: references/bounded-contexts.md

3. Entities, Value Objects, and Aggregates

Core concept: Entities have identity that persists across state changes. Value Objects are defined entirely by their attributes and are immutable. Aggregates are clusters of entities and value objects with a single root entity that enforces consistency boundaries.

Why it works: Without these distinctions, systems treat everything as a mutable, identity-bearing object with database-level relationships, leading to tangled state, inconsistent updates, and fragile concurrency. Aggregates draw a consistency boundary: everything inside is guaranteed consistent; everything outside is eventually consistent.

Key insights:

• Entity: "Am I the same thing even if all my attributes change?" (a person changes name, address, job -- still the same person)
• Value Object: "Am I defined only by my attributes?" (a $10 bill is interchangeable with any other $10 bill)
• Most things in a domain model should be Value Objects, not Entities -- prefer immutability
• Aggregate Root is the single entry point: external objects may only hold references to the root
• Keep aggregates small -- one root entity plus a minimal cluster of closely related objects
• Reference other aggregates by ID, not by direct object reference
• Design for eventual consistency between aggregates; immediate consistency only within an aggregate

Code applications:

Context	Pattern	Example
Identity tracking	Entity with ID	Order identified by orderId, survives state changes
Immutable attributes	Value Object	Address(street, city, zip) -- replace, never mutate
Consistency boundary	Aggregate Root	Order is root; OrderLine items exist only through it
Cross-aggregate reference	Reference by ID	Order stores customerId, not a Customer object
Concurrency control	Optimistic locking on root	Version field on Order; conflict if two edits race

See: references/building-blocks.md

4. Domain Events

Core concept: A domain event captures something that happened in the domain that domain experts care about. Events are named in past tense (OrderPlaced, PaymentReceived) and represent facts that have already occurred.

Why it works: Domain events decouple the cause from the effect. When OrderPlaced is published, the shipping context, billing context, and notification context can each react independently without the ordering context knowing about any of them. This reduces coupling, enables eventual consistency, and creates a natural audit trail.

Key insights:

• Name events in past tense: something that happened, not something that should happen
• Events are immutable facts -- once published, they cannot be changed or retracted
• Domain events differ from integration events: domain events are internal to a bounded context; integration events cross boundaries
• Events enable temporal decoupling: the producer does not wait for the consumer
• Event sourcing stores the full history of events as the source of truth, deriving current state by replaying them
• Not every state change needs an event -- only publish events that the domain cares about

Code applications:

Context	Pattern	Example
State transitions	Raise event on domain action	order.place() raises OrderPlaced event
Cross-context integration	Publish integration event	OrderPlaced triggers ShippingLabelRequested in shipping context
Audit trail	Store events as history	Event log: OrderPlaced -> PaymentReceived -> OrderShipped
Eventual consistency	Async event handlers	InventoryReserved handler updates stock asynchronously after OrderPlaced
Event sourcing	Rebuild state from events	Replay all Account* events to derive current account balance

See: references/domain-events.md

5. Repositories and Factories

Core concept: Repositories provide the illusion of an in-memory collection of domain objects, hiding persistence details. Factories encapsulate complex object creation logic, ensuring that aggregates are always created in a valid state.

Why it works: Domain logic should never depend on how objects are stored or constructed. Repositories abstract away SQL, ORMs, and data access so that domain code reads like business logic. Factories ensure that invariants are satisfied from the moment an aggregate is born, preventing invalid objects from ever existing.

Key insights:

• A Repository interface belongs in the domain layer; its implementation belongs in infrastructure
• Repository methods should speak the ubiquitous language: findPendingOrders(), not getByStatusCode(3)
• Collection-oriented repositories mimic add/remove; persistence-oriented repositories use save
• Factories are warranted when object creation involves complex rules, conditional logic, or assembling multiple parts
• Simple creation (a Value Object with two fields) does not need a factory -- a constructor suffices
• The Specification pattern encapsulates query criteria as domain objects: OverdueInvoiceSpecification

Code applications:

Context	Pattern	Example
Data access abstraction	Repository interface	OrderRepository.findByCustomer(customerId) in domain layer
Complex creation	Factory method	Order.createFromQuote(quote) validates and assembles from a Quote aggregate
Query encapsulation	Specification	spec = OverdueBy(days=30); repo.findMatching(spec)
Reconstitution	Repository loads aggregate	Repository assembles Order + OrderLines from DB rows into a complete aggregate
Ports and adapters	Interface in domain, impl in infra	interface OrderRepository in domain; PostgresOrderRepository in infrastructure

See: references/repositories-factories.md

6. Strategic Design and Distillation

Core concept: Not all parts of a system are equally important. Strategic design identifies the Core Domain -- the part that provides competitive advantage -- and distinguishes it from Supporting Subdomains (necessary but not differentiating) and Generic Subdomains (commodity, buy or use off-the-shelf).

Why it works: Teams that apply the same rigor to every module spread their best talent thin and over-engineer commodity functionality. By identifying the Core Domain, organizations invest their best developers, deepest modeling, and most careful design where it matters most, while using simpler approaches or third-party solutions elsewhere.

Key insights:

• Core Domain: where competitive advantage lives; invest your best people and deepest modeling here
• Supporting Subdomain: necessary for the business but not a differentiator; build it, but don't over-engineer
• Generic Subdomain: commodity functionality (authentication, email, payments); buy or use open-source
• Domain distillation extracts and highlights the Core Domain from the surrounding complexity
• A Domain Vision Statement is a short document (one page) describing the Core Domain's value proposition
• The Highlighted Core marks the most critical parts of the model so they receive the most attention
• Revisit what is "core" as the business evolves -- today's differentiator may become tomorrow's commodity

Code applications:

Context	Pattern	Example
Build vs. buy decision	Classify subdomain type	Build custom pricing engine (core); use Stripe for payments (generic)
Team allocation	Best developers on Core Domain	Senior engineers model the underwriting rules; juniors integrate the email service
Code organization	Separate core from generic	domain/pricing/ (deep model) vs. infrastructure/email/ (thin adapter)
Simplification	Distill core concepts	Extract a PolicyRatingEngine from a monolithic InsuranceService
Documentation	Domain Vision Statement	One-page doc: "Our competitive advantage is real-time risk scoring using..."

See: references/strategic-design.md

Common Mistakes

Mistake	Why It Fails	Fix
Using technical names instead of domain language	Domain logic is hidden behind DataManager and ProcessorService; experts cannot validate the model	Rename to domain terms: ClaimAdjudicator, PolicyUnderwriter; if no domain term exists, the concept may be wrong
One model to rule them all	A single Customer class serving billing, shipping, and marketing becomes bloated and contradictory	Define bounded contexts; each context gets its own Customer model with only the attributes it needs
Giant aggregates with many nested entities	Concurrency conflicts, slow loads, transactional bottlenecks	Keep aggregates small; reference other aggregates by ID; use eventual consistency between aggregates
Anemic domain model (all logic in services)	Domain objects are data bags; business rules scatter across service classes; duplication and inconsistency	Move behavior into entities and value objects; services only orchestrate, never contain domain logic
No Anti-Corruption Layer at integration points	Foreign models leak into your domain; your code becomes coupled to external schemas and naming	Wrap every external system behind a translation layer that converts to your ubiquitous language
Treating bounded contexts as microservices	Premature service extraction; distributed system complexity without the benefit	A bounded context is a model boundary, not a deployment unit; start with modules in a monolith
Skipping domain expert collaboration	Developers invent a model that does not match business reality; expensive rework	Regular modeling sessions with domain experts; refine the model until experts say "yes, that is how it works"

Quick Diagnostic

Question	If No	Action
Can a domain expert read your class names and understand them?	Code uses technical jargon instead of domain language	Rename classes, methods, and events to use ubiquitous language
Are bounded context boundaries explicitly defined?	Models bleed across boundaries; the same term means different things	Draw a context map; define explicit boundaries and translation strategies
Are aggregates small (one root + minimal cluster)?	Aggregates are large, slow, and have concurrency issues	Break into smaller aggregates; reference by ID; accept eventual consistency
Do domain objects contain behavior, not just data?	Anemic model; logic scattered in service classes	Move business rules into entities and value objects
Are domain events used for cross-aggregate communication?	Tight coupling between aggregates; synchronous chains	Introduce domain events; let aggregates react to events asynchronously
Is there an Anti-Corruption Layer at every external integration?	Foreign models pollute your domain	Add a translation layer at each integration boundary
Have you identified which subdomain is core?	Equal effort on everything; best talent spread thin	Classify subdomains; focus deep modeling on the Core Domain

Reference Files

• ubiquitous-language.md: Building a shared language, glossary maintenance, naming in code, language evolution
• bounded-contexts.md: Context boundaries, nine mapping patterns, team relationships, integration strategies
• building-blocks.md: Entities, Value Objects, Aggregates, aggregate design rules, consistency boundaries
• domain-events.md: Event naming, event sourcing, event-driven architecture, integration events
• repositories-factories.md: Repository pattern, Factory pattern, Specification pattern, ports and adapters
• strategic-design.md: Core Domain, Generic and Supporting Subdomains, distillation, build vs. buy

Further Reading

This skill is based on the Domain-Driven Design methodology developed by Eric Evans. For the complete methodology, patterns, and deeper insights, read the original book:

• "Domain-Driven Design: Tackling Complexity in the Heart of Software" by Eric Evans

About the Author

Eric Evans is a software design consultant and the originator of Domain-Driven Design. He has worked on large-scale systems in industries including finance, insurance, and logistics, where he developed the patterns and practices that became DDD. His 2003 book Domain-Driven Design: Tackling Complexity in the Heart of Software is widely regarded as one of the most influential software architecture books ever written. Evans founded Domain Language, a consulting firm that helps teams apply DDD to complex software projects. He is a frequent keynote speaker at software conferences worldwide and continues to refine and evolve DDD concepts through workshops, community engagement, and collaboration with practitioners. His work has shaped modern approaches to microservices, event sourcing, and strategic software design.