backend-engineering

Design backend systems with API patterns, service architectures, and data access.

86|9|Updated Jun 5, 2026

Search Tags:#testing #architecture #backend #microservices #patterns #databases #api-patterns

Ready to run

npx skills add https://github.com/magnus919/hermes-profiles --skill backend-engineering-magnus919

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: backend-engineering
Source: https://github.com/magnus919/hermes-profiles/tree/main/skills/backend-engineering
Command: npx skills add https://github.com/magnus919/hermes-profiles --skill backend-engineering-magnus919

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Backend teams often struggle to design scalable, maintainable server-side systems. This Skill centralizes backend engineering patterns, offering API design templates, architectural styles, data access guidelines, and testing practices to streamline delivery.

Core Features & Use Cases

API design patterns (REST, gRPC, GraphQL), service architectures (clean/hexagonal/layered), database access patterns, and middleware design, all language-agnostic.
Use cases include building modern microservices, robust APIs, and data-intensive backends with reliable testing and observability.

Quick Start

Outline a backend design for a new API service using REST and gRPC with a PostgreSQL data store.

Frequently Asked Questions about backend-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design robust backend architectures for microservices?▼

Design robust backend architectures by applying layered, clean, or hexagonal architectural patterns to establish clear service boundaries. This methodology provides structured templates for microservice ecosystems, ensuring scalable and maintainable service interactions.

What are the best API patterns for building REST, gRPC, and GraphQL endpoints?▼

The best API patterns for REST, gRPC, and GraphQL endpoints involve standardizing data access and middleware design. This approach provides language-agnostic guidelines to streamline API delivery and ensure consistent communication across data-intensive applications.

How do I manage transaction boundaries in data-intensive backend systems?▼

Manage transaction boundaries in data-intensive backend systems by implementing strict data access patterns and compatibility checks. This enforces reliable database interactions and safe integration with testing pipelines for data-heavy applications.

Does this backend engineering methodology work for language-agnostic API development?▼

Yes, this backend engineering methodology works for language-agnostic API development. It provides universal patterns for REST, gRPC, and GraphQL endpoints, along with middleware design guidelines that apply across any technology stack.

How do I set up testing pipelines for API-driven microservices?▼

Set up testing pipelines for API-driven microservices by enforcing strict safety and compatibility checks within your architecture. This methodology integrates structured documentation and testing practices to validate service reliability.

When should I use hexagonal architecture over a traditional layered approach for APIs?▼

Use hexagonal architecture over a traditional layered approach when you need to isolate business logic from data access and API endpoints. This pattern enforces stricter dependency management for highly testable and maintainable backend systems.