service-layer-design

name	service-layer-design
description	Design the service layer that coordinates business logic, transactions, and cross-cutting concerns between controllers and domain models. Outputs service interfaces, transaction boundaries, and orchestration patterns.
argument-hint	["application architecture","transaction requirements","cross-cutting concerns","team conventions"]
allowed-tools	Read, Write

Service Layer Design

The service layer sits between the presentation layer (HTTP, CLI) and the domain/data layer. It coordinates use cases, defines transaction boundaries, and handles cross-cutting concerns like logging, authorisation, and event publishing. A well-designed service layer makes business operations explicit and testable.

Process

1. One method per use case. Each service method represents a complete business operation. Not CRUD — business actions: placeOrder, processRefund, activateAccount.
2. Define transaction boundaries. Each service method is one transaction. If it fails, everything rolls back.
3. Coordinate, don't implement. Services orchestrate domain objects and repositories. Business rules live in the domain.
4. Keep services thin. If a service method exceeds 20 lines, the domain model needs richer behaviour.
5. Test service methods as units. Mock repositories and external services; test the orchestration.

Service Interface Pattern

from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Optional

# Input/output types (DTOs — not domain objects)
@dataclass
class PlaceOrderCommand:
    customer_id: str
    items: list
    payment_method_id: str
    shipping_address: str

@dataclass
class OrderResult:
    order_id: str
    status: str
    total_amount: float
    estimated_delivery: str

class OrderService(ABC):
    @abstractmethod
    async def place_order(self, cmd: PlaceOrderCommand) -> OrderResult: ...
    
    @abstractmethod
    async def cancel_order(self, order_id: str, reason: str) -> None: ...
    
    @abstractmethod
    async def process_refund(self, order_id: str) -> None: ...

Service Implementation

from core.domain import Order, OrderItem
from core.repositories import OrderRepository, CustomerRepository
from core.events import EventPublisher
from infrastructure.payments import PaymentGateway

class OrderServiceImpl(OrderService):
    def __init__(
        self,
        order_repo: OrderRepository,
        customer_repo: CustomerRepository,
        payment: PaymentGateway,
        events: EventPublisher,
    ):
        self._orders = order_repo
        self._customers = customer_repo
        self._payment = payment
        self._events = events
    
    async def place_order(self, cmd: PlaceOrderCommand) -> OrderResult:
        # 1. Load and validate domain objects
        customer = await self._customers.get(cmd.customer_id)
        if not customer:
            raise CustomerNotFoundError(cmd.customer_id)
        if not customer.can_place_orders():
            raise CustomerSuspendedError(cmd.customer_id)
        
        # 2. Build domain object (business rules enforced inside Order)
        order = Order.create(
            customer_id=cmd.customer_id,
            items=[OrderItem(pid, qty) for pid, qty in cmd.items],
            shipping_address=cmd.shipping_address,
        )
        
        # 3. External call
        charge = await self._payment.charge(order.total, cmd.payment_method_id)
          charge.success:
             PaymentDeclinedError(charge.error)
        
        
        order.confirm(transaction_id=charge.transaction_id)
         ._orders.save(order)
        
        
         ._events.publish(, {: order.})
        
         OrderResult(
            order_id=order.,
            status=order.status,
            total_amount=(order.total),
            estimated_delivery=order.estimated_delivery_date.isoformat(),
        )

Transaction Management

# Decorator-based transaction boundary
from functools import wraps

def transactional(fn):
    @wraps(fn)
    async def wrapper(self, *args, **kwargs):
        async with self._session.begin():
            return await fn(self, *args, **kwargs)
    return wrapper

class OrderServiceImpl(OrderService):
    @transactional
    async def place_order(self, cmd: PlaceOrderCommand) -> OrderResult:
        # Entire method runs in one transaction
        ...

# Or context manager pattern
class OrderServiceImpl(OrderService):
    async def place_order(self, cmd: PlaceOrderCommand) -> OrderResult:
        async with self._unit_of_work as uow:
            order = Order.create(...)
            await uow.orders.save(order)
            await uow.commit()
            # Auto-rollback on exception

Anti-Patterns to Avoid

Anti-Pattern	Problem	Fix
CRUD service methods	createOrder, updateOrder — no business meaning	Business verbs: placeOrder, confirmOrder
Business logic in service	Domain rules scattered outside domain	Domain objects enforce their own invariants
Service calling other services	Tangled dependencies, hard to test	Services coordinate domain + infra only
No transaction boundary	Partial writes on failure	One service method = one transaction
Returning domain objects	Leaks internals; tight coupling	Return DTOs; map in service layer

10 Rules

1. One service method = one business use case = one transaction.
2. Services orchestrate; domain objects implement business rules.
3. Service inputs and outputs are DTOs — never expose domain objects across the layer boundary.
4. Services depend on interfaces (repositories, gateways) — not concrete implementations.
5. Cross-cutting concerns (logging, auth checks) go in middleware or decorators — not in service methods.
6. A service method that calls another service method is a smell — consolidate into one method or extract a use case.
7. Services are stateless — all state lives in repositories.
8. Test service methods by mocking infrastructure — not by hitting the database.
9. Error types are domain errors (CustomerSuspended, PaymentDeclined) — not generic exceptions.
10. Event publishing happens at the end of the service method — after all state changes succeed.

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Deep Reference Playbook

The sections below extend this skill into a complete operating playbook so it can run end-to-end inside Claude Code, CoWork, or any agentic tool without further prompting. Pull only the sections you need for a given engagement.

Inputs the skill must collect

Before producing any output, the skill confirms:

1. Objective — the single decision or artifact the user wants out of this session.
2. Context — system, team, customer, product, or domain the work sits inside.
3. Constraints — time, budget, headcount, regulatory, technical, political.
4. Definition of done — what "good" looks like and who signs it off.
5. Audience — who reads or consumes the output (engineer, exec, customer, regulator).
6. Existing artifacts — prior versions, related docs, dashboards, tickets.
7. Risk appetite — how reversible the decision is and how much ambiguity is acceptable.

If any of these are missing, the skill asks targeted clarifying questions before generating output. It never invents constraints the user did not state.

Operating workflow

The canonical workflow for Service Layer Design runs in five stages. Each stage has an explicit exit criterion so the skill knows when to advance.

Stage 1 — Frame. Restate the problem in one paragraph. Name the decision, the deadline, the stakeholders, and the success metric. Surface assumptions explicitly so they can be challenged.

Stage 2 — Diagnose. Inventory the current state with concrete evidence: metrics, quotes, screenshots, configs, tickets. Separate facts from interpretations. Identify the two or three root causes that explain most of the gap, not the long tail of symptoms.

Stage 3 — Design. Generate at least two viable options. For each option, capture: what changes, who owns it, what it costs, what it unblocks, what it risks, and how it could fail. Recommend one with a written rationale.

Stage 4 — Execute. Convert the chosen option into a sequenced plan: milestones, owners, dependencies, gating checks, communication cadence, and rollback triggers. Anything that cannot be assigned an owner and a date is not yet a plan.

Stage 5 — Validate. Define how success will be measured, when the measurement happens, and what action follows each possible result. Schedule the retrospective before the work starts, not after.

Outputs the skill produces

Depending on the request, the skill returns one or more of:

• A one-page brief suitable for an executive reader.
• A detailed working document for the delivery team.
• A decision record capturing the choice, the alternatives, and the rationale.
• A risk register with probability, impact, owner, and mitigation.
• A sequenced action plan with named owners and explicit due dates.
• A measurement plan tied to the success metric.
• A communication plan for stakeholders inside and outside the team.

Every artifact uses clear headings, short paragraphs, and tables where comparison helps. No filler. No restating the prompt. No hedging language when a recommendation is warranted.

Decision logic and trade-offs

The skill applies the following heuristics when choices are not obvious:

• Prefer reversible decisions taken quickly over irreversible decisions taken slowly.
• Optimise for the constraint that bites first — usually time, attention, or trust, not money.
• Default to the simplest design that meets the stated definition of done; add complexity only when a specific requirement forces it.
• Make the cost of being wrong visible so the reader can judge whether the recommendation is proportionate.
• Name the people, not the roles, when assigning ownership; ambiguous ownership produces ambiguous outcomes.

Anti-patterns the skill refuses to emit

Anti-pattern	Why it fails	What the skill does instead
Generic best-practice list with no context	Reader cannot act on it	Tailors recommendations to the stated constraints
Recommendation without trade-offs	Hides the cost of being wrong	Names the price paid for the recommendation
Plan with no owners or dates	Cannot be executed or tracked	Assigns a named owner and a date to every action
Metrics theatre	Measures activity, not outcome	Ties every metric back to the user or business outcome
Boil-the-ocean scope	Nothing ships	Cuts scope to the smallest valuable slice
Buried recommendation	Reader misses the point	Leads with the recommendation in the first paragraph

Quality bar

The skill self-checks each output against these gates before returning it:

1. Can a busy executive understand the recommendation from the first 150 words?
2. Is every claim either evidenced, labelled as an assumption, or removed?
3. Does every action have an owner and a date?
4. Are the trade-offs of the recommendation stated honestly?
5. Is there a measurable success criterion?
6. Would the author be comfortable defending this artifact in a review meeting?

If any gate fails, the skill rewrites the section before returning it.

Worked micro-example

Context: Design the service layer that coordinates business logic, transactions, and cross-cutting concerns between controllers and domain models. Outputs service interf

Frame: the team needs a defensible recommendation within five working days; the audience is a cross-functional steering group; the cost of delay is higher than the cost of being slightly wrong.

Diagnose: the dominant constraint is decision latency, not analytical depth. Existing data is sufficient for a directional call.

Design: two viable options surfaced. Option A optimises for speed and reversibility. Option B optimises for completeness but slips the deadline by two weeks.

Execute: Option A recommended. Plan sequenced into a two-week sprint with named owners, a mid-point checkpoint, and a clear rollback trigger.

Validate: success measured against a single leading indicator at day 30 and a single lagging indicator at day 90. Retrospective scheduled for day 35.

Cadence and follow-through

A one-shot artifact rarely changes outcomes. The skill recommends a lightweight cadence to keep the work alive:

• Weekly: owner posts a five-line status (done, doing, blocked, risk, ask).
• Fortnightly: steering group reviews leading indicators and unblocks dependencies.
• Monthly: retrospective on what the data is teaching the team; adjust plan accordingly.
• Quarterly: revisit the original objective and decide whether to continue, pivot, or stop.

Closing rules of thumb

1. Lead with the recommendation; supporting analysis follows.
2. Treat every output as a draft that will be challenged; pre-empt the obvious objections.
3. Prefer one strong recommendation over three weak options.
4. When the evidence is thin, say so; do not launder uncertainty as confidence.
5. Optimise for the next decision, not for the perfect document.
6. Make it easy for the reader to disagree with you in a structured way.
7. Ship the artifact; iterate against feedback rather than in private.