senior-architect

$28

Quick Start

# Generate architecture diagram from project

python scripts/architecture_diagram_generator.py ./my-project --format mermaid

# Analyze dependencies for issues

python scripts/dependency_analyzer.py ./my-project --output json

# Get architecture assessment

python scripts/project_architect.py ./my-project --verbose

Tools Overview

1. Architecture Diagram Generator

Generates architecture diagrams from project structure in multiple formats.

Solves: "I need to visualize my system architecture for documentation or team discussion"

Input: Project directory path

Output: Diagram code (Mermaid, PlantUML, or ASCII)

Supported diagram types:

• component - Shows modules and their relationships

• layer - Shows architectural layers (presentation, business, data)

• deployment - Shows deployment topology

Usage:

# Mermaid format (default)

python scripts/architecture_diagram_generator.py ./project --format mermaid --type component

# PlantUML format

python scripts/architecture_diagram_generator.py ./project --format plantuml --type layer

# ASCII format (terminal-friendly)

python scripts/architecture_diagram_generator.py ./project --format ascii

# Save to file

python scripts/architecture_diagram_generator.py ./project -o architecture.md

Example output (Mermaid):

graph TD

    A[API Gateway] --> B[Auth Service]

    A --> C[User Service]

    B --> D[(PostgreSQL)]

    C --> D

2. Dependency Analyzer

Analyzes project dependencies for coupling, circular dependencies, and outdated packages.

Solves: "I need to understand my dependency tree and identify potential issues"

Input: Project directory path

Output: Analysis report (JSON or human-readable)

Analyzes:

• Dependency tree (direct and transitive)

• Circular dependencies between modules

• Coupling score (0-100)

• Outdated packages

Supported package managers:

• npm/yarn (package.json)

• Python (requirements.txt, pyproject.toml)

• Go (go.mod)

• Rust (Cargo.toml)

Usage:

# Human-readable report

python scripts/dependency_analyzer.py ./project

# JSON output for CI/CD integration

python scripts/dependency_analyzer.py ./project --output json

# Check only for circular dependencies

python scripts/dependency_analyzer.py ./project --check circular

# Verbose mode with recommendations

python scripts/dependency_analyzer.py ./project --verbose

Example output:

Dependency Analysis Report

==========================

Total dependencies: 47 (32 direct, 15 transitive)

Coupling score: 72/100 (moderate)

Issues found:

- CIRCULAR: auth → user → permissions → auth

- OUTDATED: lodash 4.17.15 → 4.17.21 (security)

Recommendations:

1. Extract shared interface to break circular dependency

2. Update lodash to fix CVE-2020-8203

3. Project Architect

Analyzes project structure and detects architectural patterns, code smells, and improvement opportunities.

Solves: "I want to understand the current architecture and identify areas for improvement"

Input: Project directory path

Output: Architecture assessment report

Detects:

• Architectural patterns (MVC, layered, hexagonal, microservices indicators)

• Code organization issues (god classes, mixed concerns)

• Layer violations

• Missing architectural components

Usage:

# Full assessment

python scripts/project_architect.py ./project

# Verbose with detailed recommendations

python scripts/project_architect.py ./project --verbose

# JSON output

python scripts/project_architect.py ./project --output json

# Check specific aspect

python scripts/project_architect.py ./project --check layers

Example output:

Architecture Assessment

=======================

Detected pattern: Layered Architecture (confidence: 85%)

Structure analysis:

  ✓ controllers/  - Presentation layer detected

  ✓ services/     - Business logic layer detected

  ✓ repositories/ - Data access layer detected

  ⚠ models/       - Mixed domain and DTOs

Issues:

- LARGE FILE: UserService.ts (1,847 lines) - consider splitting

- MIXED CONCERNS: PaymentController contains business logic

Recommendations:

1. Split UserService into focused services

2. Move business logic from controllers to services

3. Separate domain models from DTOs

Decision Workflows

Database Selection Workflow

Use when choosing a database for a new project or migrating existing data.

Step 1: Identify data characteristics

Characteristic

Points to SQL

Points to NoSQL

Structured with relationships

✓

ACID transactions required

✓

Flexible/evolving schema

✓

Document-oriented data

✓

Time-series data

✓ (specialized)

Step 2: Evaluate scale requirements

• <1M records, single region → PostgreSQL or MySQL

• 1M-100M records, read-heavy → PostgreSQL with read replicas

-

100M records, global distribution → CockroachDB, Spanner, or DynamoDB

• High write throughput (>10K/sec) → Cassandra or ScyllaDB

Step 3: Check consistency requirements

• Strong consistency required → SQL or CockroachDB

• Eventual consistency acceptable → DynamoDB, Cassandra, MongoDB

Step 4: Document decision

Create an ADR (Architecture Decision Record) with:

• Context and requirements

• Options considered

• Decision and rationale

• Trade-offs accepted

Quick reference:

PostgreSQL → Default choice for most applications

MongoDB    → Document store, flexible schema

Redis      → Caching, sessions, real-time features

DynamoDB   → Serverless, auto-scaling, AWS-native

TimescaleDB → Time-series data with SQL interface

Architecture Pattern Selection Workflow

Use when designing a new system or refactoring existing architecture.

Step 1: Assess team and project size

Team Size

Recommended Starting Point

1-3 developers

Modular monolith

4-10 developers

Modular monolith or service-oriented

10+ developers

Consider microservices

Step 2: Evaluate deployment requirements

• Single deployment unit acceptable → Monolith

• Independent scaling needed → Microservices

• Mixed (some services scale differently) → Hybrid

Step 3: Consider data boundaries

• Shared database acceptable → Monolith or modular monolith

• Strict data isolation required → Microservices with separate DBs

• Event-driven communication fits → Event-sourcing/CQRS

Step 4: Match pattern to requirements

Requirement

Recommended Pattern

Rapid MVP development

Modular Monolith

Independent team deployment

Microservices

Complex domain logic

Domain-Driven Design

High read/write ratio difference

CQRS

Audit trail required

Event Sourcing

Third-party integrations

Hexagonal/Ports &#x26; Adapters

See references/architecture_patterns.md for detailed pattern descriptions.

Monolith vs Microservices Decision

Choose Monolith when:

• Team is small (<10 developers)

• Domain boundaries are unclear

• Rapid iteration is priority

• Operational complexity must be minimized

• Shared database is acceptable

Choose Microservices when:

• Teams can own services end-to-end

• Independent deployment is critical

• Different scaling requirements per component

• Technology diversity is needed

• Domain boundaries are well understood

Hybrid approach:

Start with a modular monolith. Extract services only when:

• A module has significantly different scaling needs

• A team needs independent deployment

• Technology constraints require separation

Reference Documentation

Load these files for detailed information:

File

Contains

Load when user asks about

references/architecture_patterns.md

9 architecture patterns with trade-offs, code examples, and when to use

"which pattern?", "microservices vs monolith", "event-driven", "CQRS"

references/system_design_workflows.md

6 step-by-step workflows for system design tasks

"how to design?", "capacity planning", "API design", "migration"

references/tech_decision_guide.md

Decision matrices for technology choices

"which database?", "which framework?", "which cloud?", "which cache?"

Tech Stack Coverage

Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin, Rust

Frontend: React, Next.js, Vue, Angular, React Native, Flutter

Backend: Node.js, Express, FastAPI, Go, GraphQL, REST

Databases: PostgreSQL, MySQL, MongoDB, Redis, DynamoDB, Cassandra

Infrastructure: Docker, Kubernetes, Terraform, AWS, GCP, Azure

CI/CD: GitHub Actions, GitLab CI, CircleCI, Jenkins

Common Commands

# Architecture visualization

python scripts/architecture_diagram_generator.py . --format mermaid

python scripts/architecture_diagram_generator.py . --format plantuml

python scripts/architecture_diagram_generator.py . --format ascii

# Dependency analysis

python scripts/dependency_analyzer.py . --verbose

python scripts/dependency_analyzer.py . --check circular

python scripts/dependency_analyzer.py . --output json

# Architecture assessment

python scripts/project_architect.py . --verbose

python scripts/project_architect.py . --check layers

python scripts/project_architect.py . --output json

Getting Help

• Run any script with --help for usage information

• Check reference documentation for detailed patterns and workflows

• Use --verbose flag for detailed explanations and recommendations