Nw Fp Domain Modeling logo

Nw Fp Domain Modeling

Organization
nWave-ai
nw-fp-domain-modeling

Domain modeling with algebraic data types, smart constructors, and type-level error handling

Overview

PublishernWave-ai
RepositorynWave
Skill namenw-fp-domain-modeling
Stars
616
Forks
65
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Fp Domain Modeling AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-fp-domain-modeling
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-fp-domain-modeling .claude/skills/nw-fp-domain-modeling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Fp Domain Modeling in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nw Fp Domain Modeling on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nw Fp Domain Modeling is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

FP Domain Modeling

Domain modeling with types. Make illegal states unrepresentable, workflows as pipelines, error handling at the type level.

Cross-references: fp-principles | fp-hexagonal-architecture | fp-algebra-driven-design


1. The Two Building Blocks

[STARTER]

All domain types compose from two operations:

  • AND (Record Types): Value has ALL of these fields. Order requires CustomerInfo AND ShippingAddress AND OrderLines.
  • OR (Choice Types): Value is ONE OF these alternatives. ProductCode is either WidgetCode OR GizmoCode.

Combined recursively, these express virtually any domain structure.


2. Domain Wrappers for Primitives

[STARTER]

Never use primitives directly in the domain model. Each domain concept gets its own wrapper type.

What: Wrap primitives so the compiler distinguishes CustomerId from OrderId. When: Every primitive with domain meaning. Why: Prevents accidental mixing (compiler rejects comparing CustomerId with OrderId). Each wrapper carries its own validation rules. The type name IS the documentation.


3. Validated Construction (Smart Constructors)

[STARTER]

Raw constructor is private. A create function validates input and returns a Result type, making validation failure explicit.

Pattern: UnitQuantity must be between 1 and 1000. Its create function rejects values outside that range. A companion value function provides read access to the inner primitive.

When: Every domain wrapper with validation rules. Why: Once constructed, a value is guaranteed valid. No defensive checks deeper in the code.


4. Making Illegal States Unrepresentable

[STARTER]

The central design guideline. Instead of flags and runtime checks, model the domain so invalid states cannot be constructed.

Replace Flags with Distinct Types

Instead of { EmailAddress; IsVerified: bool }, create separate VerifiedEmailAddress and UnverifiedEmailAddress types. Functions requiring verification take VerifiedEmailAddress, making misuse a compile error.

Replace Optional Fields with Choice Types

Instead of { Email: option; Address: option } (where both could be None), create: EmailOnly | AddressOnly | EmailAndAddress. The "at least one required" rule becomes structurally enforced.

NonEmptyList for "At Least One" Rules

Define a type guaranteeing at least one element. Order with OrderLines: NonEmptyList<OrderLine> cannot have zero lines.


5. Workflows as Functions

[STARTER]

Every business workflow is a single function: Command in, Events out.

PlaceOrderWorkflow : PlaceOrderCommand -> AsyncResult<PlaceOrderEvent list>

Pipeline Composition

Workflows decompose into steps, each transforming one document type into the next:

UnvalidatedOrder -> ValidatedOrder -> PricedOrder -> Events

Each step is stateless, pure, has single input/output type, and is independently testable. The workflow assembles by piping steps together.

Why: Pipeline makes the business process visible. Each step name is a domain concept.


6. Document Lifecycle as State Types

[INTERMEDIATE]

Rather than one Order type with flags, create separate types for each lifecycle stage:

  • UnvalidatedOrder (raw input, all fields strings)
  • ValidatedOrder (all fields checked)
  • PricedOrder (prices calculated)

A top-level Order choice type unifies all states. New states (e.g., Refunded) added without breaking existing code.

When: Domain entities with distinct lifecycle stages where different data is available at each stage.


7. State Machines with Types

[INTERMEDIATE]

When an entity has distinct states with different data and different allowed operations, model each state as a separate type.

ShoppingCart = EmptyCart | ActiveCart of ActiveCartData | PaidCart of PaidCartData

Transition functions take the choice type, pattern-match on current state, return new state.

Benefits: All states explicit | each state has own data | invalid transitions prevented by types | pattern matching warnings reveal unhandled edge cases.


8. Error-Track Pipelines (Railway Pattern)

[INTERMEDIATE]

Each function returns a Result type. Pipeline short-circuits on first failure.

rawInput
  |> validateOrder           -- Result<ValidOrder, Error>
  |> bind calculateTotal     -- Result<PricedOrder, Error>
  |> bind checkInventory     -- Result<ConfirmedOrder, Error>
  |> bind chargePayment      -- Result<PaidOrder, Error>
  |> map generateReceipt     -- Result<Receipt, Error>

Key combinators:

  • map: Transform success value (one-track into two-track)
  • bind: Chain a function that itself returns Result
  • mapError: Transform error value
  • tee: Perform side effect without changing value (logging)

Error Classification

CategoryExamplesStrategy
Domain ErrorsValidation failure, out of stockModel as types, return via Result
PanicsOut of memory, null referenceThrow exceptions, catch at top level
Infrastructure ErrorsNetwork timeout, auth failureCase-by-case

Unifying Error Types

Each step may have its own error type. Define a common error choice type and use mapError to lift step errors before composing.

Collecting All Errors (Applicative Validation)

[ADVANCED]

Standard bind short-circuits on first error. For validation where you want ALL errors, use Applicative style -- runs all validations and accumulates errors into a list. See fp-principles section 5.

When: Form validation | batch input checking | any place user needs all errors at once.


9. Modeling Dependencies

[INTERMEDIATE]

Each workflow step declares exactly the functions it needs as parameters:

CheckProductCodeExists : ProductCode -> bool
GetProductPrice        : ProductCode -> Price

Convention: Dependencies first in parameter list, primary input last. Enables partial application (functional DI).

Public vs. internal: Top-level workflow hides dependencies from callers. Internal steps make them explicit.

See fp-hexagonal-architecture section 5 for full DI decision tree.


10. Persistence Ignorance

[INTERMEDIATE]

Domain model has no awareness of databases. Two distinct type hierarchies:

  • Domain types: Rich, nested, use choice types, validated wrappers. Not serialization-friendly.
  • DTO types: Flat, primitives, nullable, arrays. Designed for serialization.

Conversion Pattern

  • fromDomain: Domain -> DTO. Always succeeds.
  • toDomain: DTO -> Result<Domain, Error>. May fail (validation happens here).

Serialization as Pipeline Steps

JSON -> deserialize -> DTO -> toDomain -> [WORKFLOW] -> fromDomain -> DTO -> serialize -> JSON

Domain workflow never sees JSON or DTOs directly.


11. Bounded Contexts

[ADVANCED]

Each context has its own dialect of domain language. Contexts communicate only through events and DTOs. Design for autonomy.

Trust boundaries: Input gate validates and converts incoming DTOs to domain types. Output gate converts domain types to DTOs, deliberately dropping private information.

Inter-context relationships: Shared Kernel (shared design) | Customer/Supplier (downstream defines contract) | Anti-Corruption Layer (translator preventing external model from corrupting internal domain).


Decision Tree: How to Model This Domain Concept?

Is it a simple value with validation rules?
  YES --> Domain Wrapper + Smart Constructor
  NO -->
    Does it represent one of several alternatives?
      YES --> Choice Type (sum type)
      NO -->
        Does it group several values together?
          YES --> Record Type (product type)
          NO -->
            Does it have distinct lifecycle stages?
              YES --> State Machine with Types (section 7)
              NO -->
                Is it an operation that transforms data through stages?
                  YES --> Workflow Pipeline (section 5)
                  NO --> Evaluate whether it needs modeling at all

12. Key Design Heuristics

  1. Start from events and workflows, not data structures
  2. Let the domain expert drive naming -- their vocabulary, not technical jargon
  3. Use types to enforce business rules at compile time -- every rule in the type system needs no unit test
  4. Document effects in signatures -- Result for errors | Async for I/O | Option for missing data
  5. Separate domain types from serialization types -- domain is pure; serialization is infrastructure
  6. Prefer explicit over implicit -- every input and dependency is a function parameter

Naming Patterns

  • Types as nouns: Order | ProductCode | CustomerInfo
  • Workflows as verbs: ValidateOrder | PriceOrder | PlaceOrder
  • Events in past tense: OrderPlaced | OrderShipped
  • Commands in imperative: PlaceOrder | ShipOrder | CancelOrder
  • Lifecycle prefixes: UnvalidatedOrder | ValidatedOrder | PricedOrder

Frequently asked questions

What does the Nw Fp Domain Modeling AI skill do?

Domain modeling with algebraic data types, smart constructors, and type-level error handling

Why use Nw Fp Domain Modeling on TypingMind?

Because you install it once and use it with any model. Nw Fp Domain Modeling is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nw Fp Domain Modeling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-fp-domain-modeling. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Fp Domain Modeling?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nw Fp Domain Modeling?

As many as you like. As long as a model supports skills, you can use Nw Fp Domain Modeling with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nw Fp Domain Modeling AI skill free?

Yes. It is published on GitHub by nWave-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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