Architecture Design

When to Use

• Starting a new product or major feature that needs system design
• Choosing between monolith, modular monolith, microservices, or event-driven patterns
• Selecting a database (SQL vs NoSQL vs specialized) for a new project
• Analyzing dependencies for circular references, coupling issues, or outdated packages
• Creating architecture diagrams (Mermaid, PlantUML, ASCII) for documentation or review
• Writing Architecture Decision Records (ADRs) for technical choices
• Evaluating scalability bottlenecks or planning capacity

Context Required

From startup-context: product description, tech stack, current state (prototype/beta/scaling), team size, expected scale (users, requests/sec, data volume). If missing, ask:

• What does this system need to do? (core use cases)
• What scale are you targeting? (users, requests/sec, data size)
• What is your team size and backend experience level?
• Are there hard constraints? (compliance, latency, budget, existing infra)

Workflow

1. Gather requirements — Identify functional requirements (use cases), non-functional requirements (latency, throughput, availability, consistency), and constraints (budget, team size, compliance).
2. Run architecture assessment — Analyze the existing project structure to detect current patterns (MVC, layered, hexagonal, microservices indicators), code organization issues (god classes, mixed concerns), and layer violations.
3. Analyze dependencies — Examine the dependency tree for circular dependencies, coupling scores, and outdated packages across npm, Python, Go, or Rust projects.
4. Select architecture pattern — Use the decision workflows below to match team size, deployment needs, and data boundaries to the right pattern. For most early-stage startups, recommend modular monolith.
5. Select database — Match data characteristics, scale requirements, and consistency needs to the appropriate database technology using the selection workflow below.
6. Design data model — Produce an ER diagram in Mermaid. Define entity ownership: which module/service writes, others read via API.
7. Define API contracts — Specify key endpoints with method, path, request/response shapes, and error codes. Version from day one.
8. Generate architecture diagram — Produce a Mermaid C4 or flowchart diagram showing components, data stores, external services, and communication patterns.
9. Write ADRs — Document key decisions using the ADR format below.
10. Identify risks — Call out single points of failure, data consistency risks, and scaling bottlenecks with mitigations.

Output Format

Deliver a structured architecture document with these sections:

• Requirements Summary — Functional, non-functional, and constraints
• Architecture Assessment — Detected pattern with confidence, issues, recommendations
• System Diagram — Mermaid C4 or flowchart (component, layer, or deployment view)
• Domain Model — Mermaid ER diagram with entity ownership
• Module Boundaries — Table: Module, Responsibility, Owns Data, Exposes API
• API Contracts — Key endpoints with method, path, request/response shapes
• ADRs — Architecture Decision Records for key choices
• Dependency Analysis — Total deps, coupling score, circular deps, outdated packages
• Risks & Mitigations — Table: Risk, Impact, Likelihood, Mitigation

Frameworks & Best Practices

Architecture Pattern Selection

Default for early startups: Modular monolith with clear module boundaries that can be extracted later. Microservices add operational overhead that kills small teams.

Monolith vs Microservices Checklist

Choose Monolith when: team is small (<10), domain boundaries are unclear, rapid iteration is the priority, shared database is acceptable.

Choose Microservices when: teams can own services end-to-end, independent deployment is critical, different scaling requirements per component, domain boundaries are well understood.

Hybrid approach: Start monolith. Extract a service only when a module has significantly different scaling needs, a team needs independent deployment, or technology constraints require separation.

Database Selection

Structured data with relationships or ACID needs points to SQL. Flexible/evolving schema or document-oriented data points to NoSQL. Quick reference: PostgreSQL (default for most apps), MongoDB (document store), Redis (caching/sessions), DynamoDB (serverless auto-scaling), TimescaleDB (time-series), CockroachDB/Spanner (global distribution).

ADR Format

Use five fields: Status (Proposed/Accepted/Deprecated/Superseded), Context (what prompted the decision), Decision (what was chosen and why), Consequences (trade-offs and remaining risks), Alternatives considered (what else was evaluated and why it lost).

Key Design Principles

• Every entity has a clear owner (one module writes, others read via API)
• Use UUIDs for primary keys if multi-service or future extraction is likely
• Version APIs from day one (/v1/resource); paginate all list endpoints (cursor-based preferred)
• Consistent error shapes: { "error": { "code": "...", "message": "..." } }
• Design write operations for idempotency (idempotency keys)
• Synchronous calls (HTTP/gRPC) for queries needing immediate confirmation; async events for notifications, analytics, and cross-service data sync
• Avoid distributed transactions — use sagas or eventual consistency

Related Skills

• tech-stack-eval — Chain when choosing specific technologies for the architecture
• security-review — Chain to review the architecture for threat vectors before implementation
• code-review — Chain after implementation to verify code matches architectural intent

Examples

Prompt: "I'm building a SaaS project management tool. 3 engineers. How should I architect it?"

Good output snippet:

Given your team of 3 and early stage, I recommend a modular monolith.
Domain modules:

1. Auth Module — user accounts, roles, sessions
2. Projects Module — workspaces, projects, permissions
3. Tasks Module — tasks, assignments, comments, activity log
4. Notifications Module — email digests, in-app notifications

Each module gets its own DB schema (auth.*, projects.*) and communicates
through internal function calls with typed interfaces — not HTTP. This
gives you service-like boundaries without deployment complexity.

Architecture Assessment:
  Detected pattern: Layered Architecture (confidence: 85%)
  ✓ controllers/  - Presentation layer
  ✓ services/     - Business logic layer
  ✓ repositories/ - Data access layer

Prompt: "Should we use WebSockets or SSE for real-time collaboration?"

Good output snippet:

## ADR-001: Use WebSockets for Real-Time Collaboration
- Status: Proposed
- Context: Document editor needs sub-200ms latency for cursor positions
  and concurrent edits across 2-50 users per document.
- Decision: WebSockets via Socket.io with Redis pub/sub for horizontal
  scaling. SSE only supports server-to-client; we need bidirectional
  communication for operational transforms.
- Consequences: Adds WebSocket infrastructure (sticky sessions or Redis
  adapter), ~2KB memory per connection. Team needs OT/CRDT knowledge.
- Alternatives: SSE + POST (simpler but higher edit latency),
  Firebase Realtime DB (vendor lock-in, cost at scale).