Can I use this for database query optimization?

Yes, the skill includes specific policies to prevent N+1 queries and encourages batching and upfront pattern thinking for efficient data access.

What is the primary goal of the Atris Backend skill?

It aims to prevent over-engineering in AI-generated backend code by enforcing lean architecture policies and intent-based naming conventions.

Does this skill work with existing Atris integrations?

Yes, it is designed to work as part of the broader Atris policy system, referencing MAP.md and PERSONA.md for comprehensive context.

How does Atris handle abstractions?

It follows the 'Rule of Three,' requiring three concrete examples before allowing an abstraction to ensure patterns have truly emerged before generalizing code.

What is the Atris review command?

The 'atris review' command is a tool used within the workflow to validate your current backend implementation against the project's specific architectural policies.

Atris Backend Architecture

Name: Atris Backend Architecture
Author: atrislabs

byatrislabs

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API Development

Enforces lean, intent-driven backend architecture and prevents over-engineering in AI-generated services and APIs.

About

The Atris Backend skill provides a structured policy framework for Claude to build efficient, maintainable APIs and data access layers. By integrating with the Atris self-improving context system, it ensures that backend code follows intent-based naming conventions, avoids premature abstractions through the 'rule of three,' and implements robust error handling. This skill guides the AI to check existing project patterns in MAP.md and validate all work against specific architectural policies, ensuring that AI-generated code remains simple, consistent, and production-ready.

Key Features

Anti-N+1 query prevention and optimized data access patterns.
Rule-of-three abstraction enforcement to prevent premature complexity.
Automated policy validation using the 'atris review' workflow.
Intent-driven naming conventions to eliminate vague utility and manager classes.
Context-aware error bubbling for improved debugging and observability.
54 GitHub stars

Use Cases

Ensuring AI-generated backend services remain maintainable and free of over-engineering.
Designing consistent REST or GraphQL APIs with predictable, 'boring' patterns.
Building data access layers that prioritize performance and batching.

About

Key Features

Anti-N+1 query prevention and optimized data access patterns.
Rule-of-three abstraction enforcement to prevent premature complexity.
Automated policy validation using the 'atris review' workflow.
Intent-driven naming conventions to eliminate vague utility and manager classes.
Context-aware error bubbling for improved debugging and observability.
54 GitHub stars

Use Cases

Ensuring AI-generated backend services remain maintainable and free of over-engineering.
Designing consistent REST or GraphQL APIs with predictable, 'boring' patterns.
Building data access layers that prioritize performance and batching.