nw-code-design-fp

name	nw-code-design-fp
description	FP code-design SSOT — the WHAT-to-design catalog (algebra-driven design, domain modelling with types, railway/error-track isolation) shared by the solution architect (design-time) and the functional crafter (execution-time).

FP Code Design

Design-time catalog for functional work. The architect loads this to design the domain algebra, the type-level model, and the error/effect tracks BEFORE DISTILL authors ATs; the functional crafter loads the same SSOT for execution. This is the WHAT-to-design subset — execution mechanics (naive/discover/freeze testing, ORM/persistence mapping, language idioms) stay in nw-fp-algebra-driven-design, nw-fp-domain-modeling, and the language skills below.

FP language skills — resolve by target language, never by default

Language skill	Languages	PBT sibling (when one exists)
nw-fp-clojure	Clojure	—
nw-fp-erlang-elixir	Erlang, Elixir	nw-pbt-erlang-elixir
nw-fp-fsharp	F#	nw-pbt-dotnet
nw-fp-haskell	Haskell	nw-pbt-haskell
nw-fp-kotlin	Kotlin	nw-pbt-jvm
nw-fp-rust	Rust	nw-pbt-rust
nw-fp-scala	Scala	nw-pbt-jvm
nw-fp-typescript	TypeScript, JavaScript	nw-pbt-typescript

Same shape as the Polyglot Adapter Matrix nw-test-design-mandates-layered-mechanics already uses for PBT bindings — one row per target language, resolved from the contract's own target evidence, never guessed. A language NOT in this table has no dedicated FP-idiom skill: load this catalog and nw-fp-domain-modeling alone (both are language-agnostic) rather than silently substituting a listed language's skill — the wrong language's idioms are worse than none.

Algebra-Driven Design

Discover the API before implementing by specifying the rules (equations) operations must satisfy. Rules generate property tests, reveal missing features, and catch contradictions at design time (minutes) not production (days).

Design process (5 steps)

1. Start with scope, not implementation — do not fix data structures upfront.
2. Define observations first — how users extract information; observations define equality (two values equal if no observation distinguishes them).
3. Add operations incrementally — for each, write rules connecting it to existing ones. The web of rules IS the design.
4. Let messy rules signal problems — complex rules mean coarse building blocks; decompose until each rule is near-trivial.
5. Generalize aggressively — remove unnecessary type constraints; if operations ignore contained values, parameterize over them.

Algebraic structures (recognize → reuse known rules)

Structure	Defining rule	Design signal / use
Semigroup	(a·b)·c = a·(b·c) (associative)	Combining where parenthesization is irrelevant (concat, min/max, config merge)
Monoid	Associative + identity e·x = x = x·e	Safe defaults, fold/reduce over collections ((+,0), (concat,[]))
Semilattice	Associative + commutative + idempotent	Conflict resolution, CRDTs, eventually-consistent merges (max)
Functor	Preserves identity + composition under map	Operations agnostic to the contained type
Applicative	Element-wise combine + uniform fill	Combining containers of differing content
Group	Monoid + inverse x·x⁻¹ = e	Undo, reversible spatial transforms

Heuristic: associative? look for an identity → monoid. Have identity? check commutativity/inverse → semilattice/group. Each upgrade unlocks new rules.

API design properties (8, three categories)

Category	Properties
Clarity	Compositional · Task-relevant · Interrelated (rules link every operation)
Economy	Parsimonious · Orthogonal · Generalized (no needless type constraints)
Safety	Closed (valid construction ⇒ valid semantics) · Complete (max structure discovered)

Decision tree — is algebraic thinking worth it?

• Domain about COMBINING → rules (order/defaults/inverses) map to a known structure.
• Domain about TRANSFORMING → look for Functor / structure-preserving patterns.
• Small, well-understood surface → conventional design (algebra adds overhead).
• Otherwise → rules still clarify, even without standard structures.

Domain Modelling with Types

Make illegal states unrepresentable; model workflows as pipelines; push errors to the type level. Every rule encoded in a type needs no unit test.

Building blocks and wrappers

1. AND (record types) — value has ALL fields (Order = CustomerInfo AND Address AND OrderLines).
2. OR (choice types) — value is ONE OF alternatives (ProductCode = Widget OR Gizmo). Compose recursively to express any domain structure.
3. Domain wrappers — never use raw primitives in the domain; wrap each concept so the compiler distinguishes CustomerId from OrderId. The type name is the documentation.
4. Smart constructors — private raw constructor; a create validates and returns a Result. Once constructed, a value is guaranteed valid — no defensive checks downstream.

Make illegal states unrepresentable

Smell	Fix
{ Email; IsVerified: bool } flag	Distinct VerifiedEmail / UnverifiedEmail types; verification-requiring functions take VerifiedEmail
{ Email: option; Address: option } (both could be None)	Choice type EmailOnly | AddressOnly | EmailAndAddress — "at least one" enforced structurally
List that must be non-empty	NonEmptyList<T> — zero-element state cannot be constructed

Workflow as pipeline

Every workflow is one function: command in, events out. Decompose into stateless, pure, single-input/output steps, each transforming one document type to the next:

UnvalidatedOrder → ValidatedOrder → PricedOrder → Events

Each step name is a domain concept; each step is independently testable.

State machine with types

Model each lifecycle stage / state as a separate type; a top-level choice type unifies them (Cart = Empty | Active of ActiveData | Paid of PaidData). Transition functions pattern-match the current state and return the next. Benefits: all states explicit, per-state data, invalid transitions rejected by types, exhaustiveness warnings reveal unhandled cases. New states (e.g. Refunded) add without breaking existing code.

Dependencies and naming

• Declare each step's dependencies as leading parameters, primary input last (enables partial application = functional DI). Top-level workflow hides dependencies; internal steps make them explicit.
• Types as nouns · workflows as verbs · events past-tense · commands imperative · lifecycle prefixes (Unvalidated…/Validated…/Priced…).

Decision tree — how to model a concept?

Simple value with validation?        → Domain Wrapper + Smart Constructor
One of several alternatives?         → Choice Type (sum)
Groups several values?               → Record Type (product)
Distinct lifecycle stages?           → State Machine with Types
Transforms data through stages?      → Workflow Pipeline

Railway

Each step returns a Result; the pipeline runs on two tracks (success/failure) and short-circuits on the first failure. Design the error track up front.

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

Combinators

Combinator	Role
map	Transform the success value (one-track → two-track)
bind	Chain a function that itself returns Result
mapError	Transform the error value (lift a step error into the common type)
tee	Side effect without changing the value (logging)

Error classification (design decision per category)

Category	Examples	Strategy
Domain errors	Validation failure, out of stock	Model as types, return via Result
Panics	Out of memory, null reference	Throw; catch at top level
Infrastructure errors	Network timeout, auth failure	Case-by-case

Design rules

1. Unify error types — define one common error choice type; mapError to lift each step's error before composing.

2. Accumulate when the user needs all errors — use Applicative validation (runs all checks, collects errors into a list) for forms / batch input; standard bind short-circuits on the first.

3. Document effects in signatures — Result for errors, Async for I/O, Option for missing data; the signature is the contract.

4. Declared inputs — what does this function READ that nobody passed it?

   The FP reading of contract:declared-inputs-not-ambient-reads (SSOT: nw-cross-cutting-invariants — the gate list and the anchor live there).

   For every gate that clause lists, decide in the signature: a parameter, a reader environment, an injected capability — with the ambient lookup kept as a default the caller may state.

   A function whose result depends on state absent from its arguments is not pure, whatever its body looks like, and no amount of Result in the return type recovers that. It also cannot be property-tested honestly: the generator cannot reach the cases the ambient state is silently fixing, so the suite looks thorough while the interesting partition is unreachable.

   Test-side mirror: the Algebraic Analysis Before the Scenario mandate (nw-test-design-mandates), its declared-inputs question.

5. Count the outcomes before you choose the return type.

   How many outcomes does this operation have — and does its signature carry every one of them? Answering "it returns the value and raises on the bad case" is the wrong answer: that is N-1 outcomes in the type and one in the control flow. If any outcome is not in the return type, put it there — a sum type with one case per outcome, illegal combinations unconstructible.

   Raising is a control-transfer effect, so an operation that raises is not total. Two costs, and the second is the one that bites in a polyglot host:

   • Composition. A raising step cannot be bind-ed. Every caller needs an imperative wrapper and the railway degrades to one track with hidden exits.
   • Identity. except/catch matches on the error's . Where the same module is reachable by more than one path — a source tree plus an installed runtime, a harness that adjusts the import path, a plugin cache — that identity is not guaranteed, the handler fails to match, and a outcome escapes as a crash in an environment nobody tested. A returned sum type has no identity to mismatch.

Cross-cutting invariants (load them — they are not restated here)

Paradigm- and role-independent rules live in ONE shipped home: nw-cross-cutting-invariants. Load it alongside this skill and honour these clauses by id — they are NOT duplicated here:

• data:consumer-known-before-produced — a datum is produced only because a named consumer reads it, and you must name the JOIN KEY it will be related on. No reader, or no key → the datum is unjustified.
• gate:self-explaining-what-why-how — every rejection states WHAT / WHY / HOW.
• gate:design-principles-gdp-1-9 — the canonical gate-design contract.