Senior Architect

Complete toolkit for senior architect with modern tools and best practices.

Overview

This skill provides comprehensive system architecture capabilities through three core Python automation tools and extensive reference documentation. Whether designing microservices architectures, making technology stack decisions, or optimizing system performance, this skill delivers production-ready architectural patterns and automated analysis.

Senior architects use this skill to design scalable, maintainable systems across modern tech stacks including React, Next.js, Node.js, GraphQL, PostgreSQL, Go, Python, and cloud platforms (AWS, GCP, Azure). The skill covers microservices, clean architecture, domain-driven design, API design, performance optimization, and infrastructure planning.

Core Value: Accelerate architecture design by 60%+ while improving system scalability, maintainability, and performance through proven patterns and automated analysis tools.

Quick Start

Main Capabilities

This skill provides three core capabilities through automated scripts:

# Script 1: Architecture Diagram Generator
python scripts/architecture_diagram_generator.py [options]

# Script 2: Project Architect
python scripts/project_architect.py [options]

# Script 3: Dependency Analyzer
python scripts/dependency_analyzer.py [options]

Core Capabilities

• System Architecture Design - Design scalable, maintainable systems using microservices, clean architecture, and domain-driven design patterns
• Technology Stack Decision Making - Evaluate and select optimal technologies (React, Next.js, Node.js, GraphQL, PostgreSQL, Go, Python) based on requirements
• Architecture Diagram Generation - Automated creation of system architecture diagrams showing components, data flow, and integration patterns
• Dependency Analysis - Analyze and optimize service dependencies, identify circular dependencies, and improve modularity
• Performance & Scalability Planning - Design for horizontal scaling, caching strategies, database optimization, and load balancing
• Integration Pattern Design - Define API contracts, event-driven architectures, and service communication patterns

Python Tools

1. Architecture Diagram Generator

Automated tool for architecture diagram generator tasks.

Features:

• Automated scaffolding
• Best practices built-in
• Configurable templates
• Quality checks

Usage:

python scripts/architecture_diagram_generator.py <project-path> [options]

2. Project Architect

Comprehensive analysis and optimization tool.

Features:

• Deep analysis
• Performance metrics
• Recommendations
• Automated fixes

Usage:

python scripts/project_architect.py <target-path> [--verbose]

3. Dependency Analyzer

Advanced tooling for specialized tasks.

Features:

• Expert-level automation
• Custom configurations
• Integration ready
• Production-grade output

Usage:

python scripts/dependency_analyzer.py [arguments] [options]

Reference Documentation

Architecture Patterns

Comprehensive guide available in references/architecture_patterns.md:

• Detailed patterns and practices
• Code examples
• Best practices
• Anti-patterns to avoid
• Real-world scenarios

System Design Workflows

Complete workflow documentation in references/system_design_workflows.md:

• Step-by-step processes
• Optimization strategies
• Tool integrations
• Performance tuning
• Troubleshooting guide

Tech Decision Guide

Technical reference guide in references/tech_decision_guide.md:

• Technology stack details
• Configuration examples
• Integration patterns
• Security considerations
• Scalability guidelines

Tech Stack

Languages: TypeScript, JavaScript, Python, Go, Swift, Kotlin Frontend: React, Next.js, React Native, Flutter Backend: Node.js, Express, GraphQL, REST APIs Database: PostgreSQL, Prisma, NeonDB, Supabase DevOps: Docker, Kubernetes, Terraform, GitHub Actions, CircleCI Cloud: AWS, GCP, Azure

Key Workflows

1. System Architecture Design

Time: 2-4 hours for initial design

1. Gather Requirements - Understand functional and non-functional requirements, constraints, and success criteria
2. Identify Components - Break system into services, databases, queues, caches, and external integrations

   # Generate architecture diagram
   python scripts/architecture_diagram_generator.py --requirements requirements.md
   

3. Define Integration Patterns - Specify API contracts, event schemas, and communication protocols
4. Analyze Dependencies - Review service dependencies and data flows

   # Analyze dependencies
   python scripts/dependency_analyzer.py --services services/
   

5. Document Architecture - Create comprehensive architecture documentation with diagrams and decision records

See architecture_patterns.md for detailed patterns and examples.

2. Technology Stack Selection

Time: 1-2 hours per major technology decision

1. Define Criteria - List requirements (performance, scalability, team expertise, ecosystem, cost)
2. Research Options - Evaluate 3-5 technology options against criteria
3. Prototype & Benchmark - Build proof-of-concept implementations
4. Document Decision - Create Architecture Decision Record (ADR) with rationale

See tech_decision_guide.md for evaluation frameworks.

3. Microservices Architecture Implementation

Time: 1-2 weeks for initial setup

1. Service Boundary Definition - Apply domain-driven design to identify bounded contexts
2. Infrastructure Setup - Configure Docker, Kubernetes, service mesh, and observability

   # Generate project architecture
   python scripts/project_architect.py --pattern microservices
   

3. API Gateway Configuration - Setup routing, authentication, rate limiting
4. Deploy & Monitor - Deploy services and establish monitoring dashboards

4. Performance Optimization

Time: 2-3 days per optimization cycle

1. Establish Baselines - Measure current performance metrics (latency, throughput, resource usage)
2. Identify Bottlenecks - Use profiling tools and analysis scripts

   # Analyze system dependencies and bottlenecks
   python scripts/dependency_analyzer.py --analyze-performance
   

3. Implement Optimizations - Apply caching, database indexing, query optimization, code improvements
4. Validate Improvements - Measure impact and document optimizations

Development Workflow

1. Setup and Configuration

# Install dependencies
npm install
# or
pip install -r requirements.txt

# Configure environment
cp .env.example .env

2. Run Quality Checks

# Use the analyzer script
python scripts/project_architect.py .

# Review recommendations
# Apply fixes

3. Implement Best Practices

Follow the patterns and practices documented in:

• references/architecture_patterns.md
• references/system_design_workflows.md
• references/tech_decision_guide.md

Best Practices Summary

Code Quality

• Follow established patterns
• Write comprehensive tests
• Document decisions
• Review regularly

Performance

• Measure before optimizing
• Use appropriate caching
• Optimize critical paths
• Monitor in production

Security

• Validate all inputs
• Use parameterized queries
• Implement proper authentication
• Keep dependencies updated

Maintainability

• Write clear code
• Use consistent naming
• Add helpful comments
• Keep it simple

Common Commands

# Development
npm run dev
npm run build
npm run test
npm run lint

# Analysis
python scripts/project_architect.py .
python scripts/dependency_analyzer.py --analyze

# Deployment
docker build -t app:latest .
docker-compose up -d
kubectl apply -f k8s/

Troubleshooting

Common Issues

Check the comprehensive troubleshooting section in references/tech_decision_guide.md.

Getting Help

• Review reference documentation
• Check script output messages
• Consult tech stack documentation
• Review error logs

Resources

• Pattern Reference: references/architecture_patterns.md
• Workflow Guide: references/system_design_workflows.md
• Technical Guide: references/tech_decision_guide.md
• Tool Scripts: scripts/ directory