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architecture-principles

Apply AI-era software design principles to guide architecture decisions.

Updated Mar 23, 2026
Search Tags:#testing#architecture#refactoring#principles#software-design#code-organization#ai-collaboration
One-click install
npx skills add https://github.com/0xEdenY/eden-claude-skills --skill architecture-principles-0xedeny
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: architecture-principles
Source: https://github.com/0xEdenY/eden-claude-skills/tree/main/architecture-principles
Command: npx skills add https://github.com/0xEdenY/eden-claude-skills --skill architecture-principles-0xedeny

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI-era software design principles and AI collaboration workflows are often implicit; this Skill codifies core design tenets to guide architecture decisions and collaboration across teams.

Core Features & Use Cases

  • Principles: Spec-first, explicit constraints, deletable design, data-model-first, clean boundaries, failure-aware design, and managed evolution.
  • AI collaboration workflows: Context engineering, verifiable incremental delivery, and review/learn loops to improve AI outputs and governance.
  • Use Case: Teams building robust AI-driven systems with clear guidelines, ADRs, and CLAUDE.md rules to ensure maintainable growth.

Quick Start

Apply these seven principles to your project by aligning CLAUDE.md rules, ADRs, and context-engineering workflows to start practicing architecture governance today.

Frequently Asked Questions about architecture-principles

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

FAQPage Schema
How do I enforce software architecture principles during AI code generation?▼

To enforce software architecture principles during AI code generation, apply structured guidance via CLAUDE.md-like rules and ADRs. This codifies design tenets like spec-first, clean boundaries, and managed evolution to govern AI collaboration workflows.

What are AI-era software design principles for code organization?▼

AI-era software design principles for code organization include spec-first constraints, deletable design, data-model-first approaches, explicit boundaries, failure-aware design, and managed evolution to ensure maintainable system growth.

How do I set up context engineering workflows for AI collaboration?▼

Set up context engineering workflows for AI collaboration by implementing verifiable incremental delivery and review or learn loops. This structures AI outputs and enforces architecture governance through frontmatter-defined metadata and principled boundaries.

Do I need Architecture Decision Records to manage system evolution?▼

You need Architecture Decision Records to manage system evolution by documenting principled boundaries and failure handling. This provides structured governance over refactoring and code organization as AI-driven systems scale.

What is the best way to automate architecture decisions in software design?▼

The best way to automate architecture decisions in software design is codifying core tenets into frontmatter-defined metadata and CLAUDE.md rules. This enforces failure-aware design and data modeling without manual review bottlenecks.

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