review-architecture

Automate architectural review of source code for layering and consistency issues.

92|7|Updated Dec 11, 2025

Search Tags:#code analysis #software design #architecture review #layering #single-source-of-truth

Ready to run

npx skills add https://github.com/encero-systems/incan --skill review-architecture-encero-systems

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: review-architecture
Source: https://github.com/encero-systems/incan/tree/main/.agents/skills/review-architecture
Command: npx skills add https://github.com/encero-systems/incan --skill review-architecture-encero-systems

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the process of reviewing architectural patterns in software systems, identifying and reporting issues such as layering violations, policy drift, and semantic inconsistencies.

Core Features & Use Cases

Architecture Review Automation: Automatically scan codebases for architectural problems like incorrect layering and single-source-of-truth violations.
Detailed Reporting: Generates detailed reports on the architecture, highlighting issues with specific references to source code and line numbers.
Use Case: Use this Skill in a development workflow to ensure that code changes adhere to architectural best practices, reducing the risk of technical debt.

Quick Start

Run the review-architecture skill to automatically check the codebase architecture and report findings.

Frequently Asked Questions about review-architecture

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

FAQPage Schema

How do I automate architecture review to find layering violations in my codebase?▼

Architecture review automation scans your source code to identify incorrect layering, single-source-of-truth violations, and semantic inconsistencies. It parses the codebase to detect architectural patterns, generating detailed reports with specific source code references and line numbers.

What is single-source-of-truth violation detection and how does it work?▼

Single-source-of-truth violation detection analyzes source code to locate semantic inconsistencies and policy drift where data or logic is duplicated incorrectly. It parses the codebase to trace architectural patterns and flags violations, reporting specific file references and line numbers.

How do I check code changes for architectural best practices in a development workflow?▼

Checking code changes for architectural best practices involves scanning the codebase to detect layering violations and single-source-of-truth issues. This reduces technical debt risk by ensuring new code adheres to established architectural standards through automated pattern analysis.

Can I use automated code analysis to report semantic inconsistencies with source code references?▼

Automated code analysis reports semantic inconsistencies by parsing and analyzing source code for architectural patterns. It generates detailed reports highlighting issues with specific references to source code and line numbers, identifying problems like incorrect layering and policy drift.

What are the limitations of automating architectural pattern detection?▼

Automating architectural pattern detection requires parsing and analyzing source code, meaning its effectiveness depends on codebase structure and language support. It identifies layering, single-source-of-truth, and semantic issues but may require integration into existing development workflows to reduce technical debt effectively.