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kcmvp merged 1 commit into from
Sep 17, 2025
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#40: milestone for -code-as-promote #42

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17 changes: 11 additions & 6 deletions README.md
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## Key Features

* **Declarative, Fluent API:** Define architectural rules in a clear and readable way.
* **Pre-defined Rules:** Get started quickly with a comprehensive set of rules for common Go best practices.
* **Custom Rules:** Easily create your own custom rules to enforce project-specific constraints.
* **Layered Architecture Validation:** Define and enforce dependencies between architectural layers.
* **Dependency Checking:** Prevent illegal dependencies between packages, types, and layers.
* **Naming Convention Enforcement:** Ensure your code follows consistent naming conventions.
* **Declarative, Fluent API:** `archunit` provides a fluent and declarative API that allows you to define architectural rules in a clear, readable, and chainable way. This makes your architecture tests easy to understand and maintain.

* **Functional Approach:** The library promotes a functional style by treating rules as first-class citizens. You can define, combine, and pass rules as functions, leading to more modular and reusable architecture tests.

* **Generic Support:** `archunit` leverages Go generics to provide type-safe and reusable selections and rules. This reduces boilerplate code, improves type safety, and makes your architecture tests more robust.

* **Rich Pre-defined Rules:** Get started quickly with a comprehensive set of pre-defined rules for common Go best practices. These rules cover a wide range of checks, from naming conventions and package structure to dependency management and API design.

* **Code as Promotion: AI-Guided Development:** `archunit` introduces a "Code as Promotion" paradigm, where your architectural rules act as a direct guide for AI code generation.
* **AI-Friendly Code Style:** The declarative and readable rules serve as a machine-readable design specification. This guides AI tools to generate code that is always aligned with your intended architecture.
* **Native AI Feedback Loop:** When a rule is violated, the assertion output is structured as a clear and actionable prompt. This "promotion" can be fed directly back to the AI, enabling it to learn from its mistakes and automatically correct the code, creating a powerful and efficient development feedback loop.

## Installation

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