AWS AI-Driven Development Template

Enterprise-grade project template with integrated AI coding assistant configurations and AWS best practices. Generate production-ready applications with hexagonal architecture in minutes.

1. The Challenge and Solution

The Challenge

AI coding assistants often produce inconsistent code because they lack project-specific context and standards. Teams spend more time fixing AI-generated code than writing it themselves.

The Solution

This template provides comprehensive project context through:

• Standardized development rules and patterns
• Pre-configured AI assistant settings
• Production-ready architecture patterns
• Automated security and quality checks and testing

2. How to Use the Solution

Quick Start

# Install dependencies
pip install cruft

# Create new project
cruft create https://github.com/aws-samples/sample-ai-coding-standards-template.git --directory template/

# Follow the prompts, then:
cd your-project-name
open README.md

What you get: A complete AWS Hello World application with Lambda functions, DynamoDB, CDK infrastructure, integration tests, and AI assistant configurations for Amazon Q, Cursor, Cline, and more.

Generated Project Structure

your-project/
├── src/
│   ├── functions/           # Lambda functions
│   ├── shared/             # Business logic & adapters
│   └── tests/              # Integration tests
├── infrastructure/         # CDK stacks
├── .amazonq/              # Amazon Q configuration
├── .cursor/               # Cursor AI configuration
├── .cline/                # Cline configuration
└── Taskfile.yml           # Automation commands

AI Assistant Configurations

Pre-configured for multiple AI coding assistants:

• Amazon Q Developer - AWS-native development
• Cursor AI - Code completion and chat
• Cline - Autonomous coding agent
• Roo Cline - Enhanced Claude integration
• Kiro AI - Advanced code analysis

Architecture Patterns

• Hexagonal Architecture - Clean separation between domain logic, ports, and adapters
• Domain-Driven Design - Business logic first, independent of infrastructure
• AWS CDK - Infrastructure as code with enterprise-grade patterns
• Integration Testing - Real AWS resource testing with tag-based discovery
• Clean Imports - No complex path management, full IDE support

Key Benefits

For Development Teams:

• Productivity - AI assistants understand project context without extensive guidance
• Quality - Standardized patterns ensure consistent, maintainable code
• Cohérence - Shared vocabulary between AI assistants and human developers
• Scalability - Enterprise-grade architecture that grows with your needs

For AI Assistants:

• Better Context - Comprehensive project knowledge and architectural patterns
• Accurate Suggestions - Code follows established standards from creation
• Reduced Corrections - Less time spent reviewing and fixing AI-generated code

3. How to Customize the Solution

Update AI Assistant Rules

Modify development standards in rules/:

# Edit any rule file
vim rules/aws.md

# Apply changes to all AI assistants
task install

Update MCP Server Configuration

Add or modify AI assistant capabilities in mcp/mcp.json:

# Edit MCP configuration
vim mcp/mcp.json

# Apply changes
task install

Update Hooks

Modify AI assistant hooks in hooks/:

# Edit hook files
vim hooks/architecture-decision-record.hook

# Apply changes
task install

Keep Projects Updated

# Update existing projects with template improvements
task cruft:update

Prerequisites

• Python 3.11+
• Task for automation
• AWS CLI configured
• Git for version control

License

This configuration is provided as-is for development use.