architect

name	architect
description	Design system architecture and high-level technical strategy
allowed-tools	["Read","Grep","Glob","Bash"]

Architecture Design

Design system architecture and make strategic technical decisions.

Core Principle

Good architecture enables change while maintaining simplicity.

Name

han-core:architect - Design system architecture and high-level technical strategy

Synopsis

/architect [arguments]

Architecture vs Planning

Architecture Design (this skill):

• Strategic: "How should the system be structured?"
• Component interactions and boundaries
• Technology and pattern choices
• Long-term implications
• System-level decisions

Technical Planning:

• Tactical: "How do I implement feature X?"
• Specific implementation tasks
• Execution details
• Short-term focus

Use /architect when:

• Designing new systems or subsystems
• Significant system change (new subsystem, major refactor)
• Affects multiple components or teams
• Major refactors affecting multiple components
• Technology selection decisions
• Defining system boundaries and interfaces
• Long-term technical strategy needed
• Need to evaluate multiple approaches
• Decisions have broad impact

Use /plan when:

• Implementing within existing architecture
• Implementing specific feature within existing architecture
• Tactical execution planning
• Breaking down known work
• Architecture is already decided
• Task sequencing and execution

Architecture Process

1. Understand Context

Business context:

• What problem are we solving?
• Who are the users?
• What are the business goals?
• What are the success metrics?

Technical context:

• What exists today?
• What constraints exist?
• What must we integrate with?
• What scale must we support?

Team context:

• What's our expertise?
• What can we maintain?
• What's our velocity?

2. Gather Requirements

Functional requirements:

• What must the system do?
• What are the features?
• What are the user scenarios?

Non-functional requirements:

• Performance: Response time, throughput
• Scalability: Expected load, growth
• Availability: Uptime requirements
• Security: Compliance, data protection
• Maintainability: Team size, skills
• Cost: Budget constraints

Example:

## Requirements

### Functional
- Users can search products by name/category
- Users can add items to cart
- Users can checkout and pay

### Non-Functional
- Search response time < 200ms (p95)
- Support 10,000 concurrent users
- 99.9% uptime
- PCI DSS compliant for payments
- Team of 5 developers can maintain

3. Identify Constraints

Technical constraints:

• Must use existing authentication system
• Must integrate with legacy inventory system
• Database must be PostgreSQL (existing infrastructure)

Business constraints:

• Budget for infrastructure
• Must support EU data residency

Team constraints:

• Team experienced in Python, less in Go
• No DevOps specialist on team
• Remote team across timezones

4. Consider Alternatives

Never design in a vacuum - consider options:

Example: Data storage choice

Option 1: PostgreSQL

• Pros: Team knows it, ACID guarantees, rich query support
• Cons: Vertical scaling limits, setup complexity

Option 2: MongoDB

• Pros: Flexible schema, horizontal scaling
• Cons: Team unfamiliar, eventual consistency

Option 3: DynamoDB

• Pros: Fully managed, auto-scaling
• Cons: Vendor lock-in, query limitations, cost at scale

Decision: PostgreSQL

• Team expertise outweighs scaling concerns
• Can re-evaluate if scale becomes issue
• Faster initial development

5. Design System Structure

Define components and their responsibilities:

┌─────────────────────────────────────────────┐
│             Client Apps                      │
│  (Web, iOS, Android)                         │
└────────────────┬────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────┐
│          API Gateway / Load Balancer         │
└────────────────┬────────────────────────────┘
                 │
        ┌────────┴────────┐
        ▼                 ▼
┌───────────────┐  ┌───────────────┐
│   Auth        │  │   Core API     │
│   Service     │  │   Service      │
└───────┬───────┘  └───────┬───────┘
        │                  │
        │         ┌────────┴────────┐
        │         ▼                 ▼
        │  ┌──────────────┐  ┌──────────────┐
        │  │  PostgreSQL  │  │   Redis      │
        │  │  (Primary)   │  │   (Cache)    │
        │  └──────────────┘  └──────────────┘
        │
        ▼
┌───────────────┐
│   User DB     │
└───────────────┘

Component descriptions:

## Components

### API Gateway
**Responsibility:** Route requests, rate limiting, authentication
**Technology:** Nginx
**Dependencies:** Auth Service, Core API Service
**Scale:** 2-3 instances behind load balancer

### Auth Service
**Responsibility:** User authentication, session management, JWT issuing
**Technology:** Python (Flask), PostgreSQL
**API:** REST
**Scale:** Stateless, 2-N instances

### Core API Service
**Responsibility:** Business logic, data access, external integrations
**Technology:** Python (FastAPI), PostgreSQL, Redis
**API:** REST
**Scale:** Stateless, 2-N instances

### PostgreSQL
**Responsibility:** Primary data store
**Scale:** Primary with read replica

### Redis
**Responsibility:** Session storage, caching, rate limiting
**Scale:** Cluster mode (3 nodes)

6. Define Interfaces

API contracts:

## API Design

### POST /api/auth/login
**Purpose:** Authenticate user, issue JWT

**Request:**
```json
{
  "email": "[e-mail]",
  "password": "secure_password"
}

Response (200):

{
  "token": "eyJ...",
  "user": {
    "id": "123",
    "email": "[e-mail]",
    "name": "John Doe"
  }
}

Errors:

• 400: Invalid request
• 401: Invalid credentials
• 429: Rate limit exceeded

### 7. Plan for Failure

**What can go wrong?**
- Database unavailable
- External API down
- Network partition
- High load
- Data corruption

**Mitigation strategies:**
- Retry with exponential backoff
- Circuit breakers for external services
- Graceful degradation
- Health checks and monitoring
- Database backups

**Example:**
```markdown
## Failure Scenarios

### Database Unavailable
**Impact:** Cannot read/write data
**Mitigation:**
- Read replica failover (automated)
- Circuit breaker after 3 failures
- Cache serves stale data for 5 minutes
- User sees degraded experience message
**Recovery:** Manual failover to replica, fix primary

### External Payment API Down
**Impact:** Cannot process payments
**Mitigation:**
- Retry 3 times with exponential backoff
- Queue payments for later processing
- User notified of delay
- Alert on-call engineer
**Recovery:** Process queued payments once API recovers

8. Document Decisions

Architecture Decision Record (ADR):

# ADR-001: Use PostgreSQL for Primary Database

**Status:** Accepted
**Date:** 2024-01-15
**Deciders:** Tech Lead, Backend Team

## Context

We need to choose a primary database for user data, products, and orders.

Requirements:
- Strong consistency (ACID)
- Complex queries (joins, aggregations)
- < 200ms query time for 90% of queries
- Support 100k users initially

## Decision

Use PostgreSQL as primary database.

## Alternatives Considered

### MongoDB
- **Pros:** Flexible schema, horizontal scaling
- **Cons:** Team unfamiliar, eventual consistency issues
- **Why not:** Team expertise more valuable than flexibility

### DynamoDB
- **Pros:** Managed service, auto-scaling
- **Cons:** Vendor lock-in, limited query capability, cost
- **Why not:** Query limitations would hurt development velocity

### MySQL
- **Pros:** Similar to PostgreSQL, team knows it
- **Cons:** Less feature-rich than PostgreSQL
- **Why not:** PostgreSQL offers JSON support, better full-text search

## Consequences

**Positive:**
- Team can be productive immediately
- Strong consistency guarantees
- Rich query capabilities
- JSON support for flexible data

**Negative:**
- Vertical scaling limits (mitigated: can add read replicas)
- More complex than managed services (mitigated: use RDS)
 Higher operational overhead


 Chose familiarity over horizontal scaling
 Chose rich queries over eventual consistency
 Can re-evaluate if scale requirements change



 Team confirmed expertise in PostgreSQL
 Load testing shows meets performance requirements
 Cost analysis shows acceptable for first year

Architecture Document Structure

# Architecture Design: [System/Feature Name]

## Context

### Problem Statement
[What business problem are we solving?]

### Goals
[What are we trying to achieve?]

### Non-Goals
[What are we explicitly NOT trying to achieve?]

### Requirements

**Functional:**
- [Requirement 1]
- [Requirement 2]

**Non-Functional:**
- Performance: [e.g., < 200ms response time]
- Scalability: [e.g., handle 10k concurrent users]
- Security: [e.g., PCI compliance]
- Maintainability: [e.g., easy to modify]

### Constraints
[Technical, time, resource, or business constraints]

## Current Architecture
[What exists today? What needs to change?]

## Proposed Architecture

### High-Level Design

[Diagram or description of system components]

┌─────────────┐ ┌─────────────┐ ┌──────────────┐ │ Client │────▶│ API │────▶│ Database │ └─────────────┘ └─────────────┘ └──────────────┘ │ ▼ ┌─────────────┐ │ Cache │ └─────────────┘

### Components

#### Component A
**Responsibility:** [What it does]
**Interface:** [How others interact with it]
**Dependencies:** [What it depends on]
**Technology:** [Implementation stack]

#### Component B
[Similar structure...]

### Data Flow
[How data moves through the system]

### API Design
[Key endpoints, schemas, contracts]

### Data Model
[Database schema, key entities]

### Security Model
[Authentication, authorization, data protection]

## Alternative Approaches Considered

### Alternative 1: [Approach name]
**Pros:**
- [Advantage 1]
- [Advantage 2]

**Cons:**
- [Disadvantage 1]
- [Disadvantage 2]

**Why not chosen:** [Reasoning]

### Alternative 2: [Another approach]
[Similar structure...]

## Decision Rationale

### Why This Architecture?
[Explain the key decisions and trade-offs]

### Trade-offs Accepted
[What we gave up for what benefits]

### Assumptions
[What we're assuming to be true]

## Implementation Strategy

### Phase 1: [Foundation]
[What to build first]

### Phase 2: [Core Features]
[Next phase]

### Phase 3: [Enhancement]
[Final phase]

### Migration Strategy
[If replacing existing system, how to transition?]

## Risks & Mitigation

| Risk | Impact | Probability | Mitigation |
|------|--------|-------------|------------|
| [Risk 1] | High | Medium | [How to mitigate] |
| [Risk 2] | Medium | Low | [How to mitigate] |

## Testing Strategy
[How will we validate this architecture?]

## Monitoring & Observability
[How will we know if it's working?]

## Success Metrics
[How will we measure success?]

## Open Questions
[What still needs to be resolved?]

## References
[Links to research, related docs, RFCs]

Architecture Principles

1. Simplicity

Start simple, add complexity only when needed.

BAD: Microservices from day 1 with 20 services
GOOD: Start with monolith, split when needed

Apply YAGNI: You Aren't Gonna Need It

• Don't build for hypothetical future
• Add when actually needed
• Simpler is easier to maintain

2. Separation of Concerns

Each component has one clear responsibility.

GOOD:
- Auth Service: Authentication only
- User Service: User profile management
- Order Service: Order processing

BAD:
- God Service: Does everything

Apply SOLID principles:

• Single Responsibility
• Open/Closed
• Liskov Substitution
• Interface Segregation
• Dependency Inversion

3. Loose Coupling

Components depend on interfaces, not implementations.

// BAD: Tight coupling
class OrderService {
  constructor(private db: PostgresDatabase) {}
}

// GOOD: Loose coupling
class OrderService {
  constructor(private db: Database) {}  // Interface
}

Benefits:

• Easier to test (mock interface)
• Easier to swap implementations
• Components can evolve independently

4. High Cohesion

Related functionality stays together.

GOOD:
user/
  - create_user.ts
  - update_user.ts
  - delete_user.ts
  - user_repository.ts

BAD:
create/
  - create_user.ts
  - create_order.ts
update/
  - update_user.ts
  - update_order.ts

5. Explicit Over Implicit

Make dependencies and contracts clear.

// BAD: Implicit dependency
function processOrder(orderId: string) {
  const db = global.database  // Where does this come from?
  // ...
}

// GOOD: Explicit dependency
function processOrder(
  orderId: string,
  db: Database,
  logger: Logger
) {
  // Dependencies are clear
}

6. Fail Fast

Detect and report errors early.

// BAD: Silent failure
function divide(a: number, b: number) {
  if (b === 0) return 0  // Wrong!
  return a / b
}

// GOOD: Fail fast
function divide(a: number, b: number) {
  if (b === 0) {
    throw new Error('Division by zero')
  }
  return a / b
}

7. Design for Testability

Make it easy to test.

// BAD: Hard to test
class OrderService {
  processOrder(orderId: string) {
    const db = new PostgresDatabase()  // Can't mock
    const api = new PaymentAPI()       // Can't mock
    // ...
  }
}

// GOOD: Easy to test
class OrderService {
  constructor(
    private db: Database,      // Can inject mock
    private api: PaymentAPI    // Can inject mock
  ) {}

  processOrder(orderId: string) {
    // ...
  }
}

Common Architecture Patterns

Layered Architecture

┌─────────────────────┐
│  Presentation       │ (UI, API controllers)
├─────────────────────┤
│  Business Logic     │ (Domain, services)
├─────────────────────┤
│  Data Access        │ (Repositories, ORMs)
├─────────────────────┤
│  Database           │ (Storage)
└─────────────────────┘

When to use: Simple to moderate complexity

Hexagonal Architecture (Ports & Adapters)

        ┌───────────────────────┐
        │   External Systems    │
        │  (UI, DB, APIs)       │
        └──────────┬────────────┘
                   │
        ┌──────────▼────────────┐
        │      Adapters         │ (Implementation)
        │  (REST, PostgreSQL)   │
        └──────────┬────────────┘
                   │
        ┌──────────▼────────────┐
        │       Ports           │ (Interfaces)
        │  (IUserRepo, IAuth)   │
        └──────────┬────────────┘
                   │
        ┌──────────▼────────────┐
        │    Core Domain        │ (Business logic)
        │    (Pure logic)       │
        └───────────────────────┘

When to use: Want to isolate business logic, multiple frontends

Microservices

┌─────────┐  ┌─────────┐  ┌─────────┐
│  User   │  │  Order  │  │ Payment │
│ Service │  │ Service │  │ Service │
└────┬────┘  └────┬────┘  └────┬────┘
     │            │            │
     └────────────┴────────────┘
                  │
          ┌───────▼────────┐
          │  Message Bus   │
          │  (Event-driven)│
          └────────────────┘

When to use: Large team, need independent deploy, clear boundaries

Avoid when: Small team, unclear boundaries, early stage

Event-Driven Architecture

┌─────────┐       ┌─────────────┐       ┌─────────┐
│Producer │──────▶│ Event Bus   │──────▶│Consumer │
└─────────┘       └─────────────┘       └─────────┘

When to use: Async processing, decoupled systems, audit trails

Architectural Patterns to Consider

System Patterns:

• Layered architecture
• Microservices vs monolith
• Event-driven architecture
• CQRS (Command Query Responsibility Segregation)
• Hexagonal architecture

Data Patterns:

• Database per service
• Shared database
• Event sourcing
• CQRS
• Cache-aside

Integration Patterns:

• API Gateway
• Service mesh
• Message queue
• Pub/sub
• GraphQL federation

Anti-Patterns

Premature Optimization

Don't optimize for scale you don't have.

BAD: Build microservices for 100 users
GOOD: Start with monolith, split when needed

Resume-Driven Architecture

Don't choose technology to pad resume.

BAD: "I want to learn Kubernetes, let's use it"
GOOD: "Kubernetes fits our scale needs"

Distributed Monolith

Microservices that are tightly coupled.

BAD: Service A can't deploy without Service B
GOOD: Services are independently deployable

Big Ball of Mud

No structure, everything depends on everything.

BAD: Any code can call any other code
GOOD: Clear layers and boundaries

Analysis Paralysis

Over-analyzing, never shipping.

BAD: Spend months on perfect architecture
GOOD: Design enough to start, iterate

Architecture Review Checklist

• Business goals clearly understood
• Functional requirements documented
• Non-functional requirements defined
• Constraints identified
• Multiple alternatives considered
• Trade-offs explicitly stated
• Component responsibilities clear
• Interfaces well-defined
• Data flow documented
• Failure scenarios planned for
• Security model defined
• Scalability addressed
• Testability designed in
• Decisions documented (ADRs)
• Implementation phases outlined
• Team can implement and maintain
• Success metrics defined

Examples

When the user says:

• "Design the architecture for our multi-tenant system"
• "How should we structure our microservices?"
• "Plan the technical approach for real-time notifications"
• "Design the data model for our marketplace"
• "Create architecture for migrating from monolith to services"

Integration with Other Skills

• Apply solid-principles - Guide component design
• Apply simplicity-principles - KISS, YAGNI
• Apply orthogonality-principle - Independent components
• Apply structural-design-principles - Composition patterns
• Use technical-planning - For implementation after design

Notes

• Use TaskCreate to track architecture design steps
• Apply all relevant design principle skills
• Create diagrams (ASCII art or reference drawing tools)
• Architecture evolves - document decisions and changes
• Consider using /plan for detailed implementation after architecture is approved
• Archive decisions as ADRs for future reference

Remember

1. Simplicity first - Start simple, add complexity when needed
2. Document decisions - Future you will thank you
3. Consider alternatives - Never the first idea only
4. State trade-offs - Every decision has consequences
5. Design for change - Systems evolve

The best architecture is the one that's simple enough to ship and flexible enough to evolve.