codebase-architecture-doc

Enforce repository architecture rules for domain-isolated source code and pipeline separation.

Updated Aug 27, 2026

Search Tags:#architecture #development workflow #codebase #ai context #code organization #domain isolation

Ready to run

npx skills add https://github.com/Rukkha1024/muscle_synergy_analysis --skill codebase-architecture-doc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: codebase-architecture-doc
Source: https://github.com/Rukkha1024/muscle_synergy_analysis/tree/main/.agents/skills/codebase-architecture-doc
Command: npx skills add https://github.com/Rukkha1024/muscle_synergy_analysis --skill codebase-architecture-doc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and agents (resource) components.

What problem does it solve?

This Skill ensures adherence to a well-defined codebase architecture, preventing code sprawl and ensuring AI models receive the correct context for development tasks.

Core Features & Use Cases

Architecture Enforcement: Guides developers on where to place code (domain-isolated src/), separating pipeline (scripts/) from analysis (analysis/), and standardizing configuration (configs/) and output (outputs/) conventions.
AI Context Management: Helps determine the minimal, relevant code context to provide to AI coding assistants like Codex, improving efficiency and accuracy.
Use Case: When adding a new feature, this Skill helps you decide whether to modify existing logic in src/, add a new pipeline step in scripts/, or create a new analysis in analysis/, and guides you on what files to share with the AI.

Quick Start

Use the codebase-architecture-doc skill to determine the correct folder structure and context for my code changes.

Frequently Asked Questions about codebase-architecture-doc

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

FAQPage Schema

How do I organize my codebase to provide better context for AI coding assistants?▼

To provide better AI context, organize your codebase by enforcing domain-isolated source code in `src/`, separating pipeline scripts in `scripts/` from analysis in `analysis/`, and standardizing YAML configurations in `configs/`.

What is the best way to separate data pipeline scripts from analysis code in a repository?▼

The best way to separate pipeline and analysis code is to enforce repository architecture rules that isolate pipeline steps in a dedicated `scripts/` directory and place analytical logic in a separate `analysis/` folder.

How do I decide where to place new code when adding a feature to a domain-isolated codebase?▼

When adding a feature, place new domain logic in `src/`, add new pipeline steps in `scripts/`, or create new analysis in `analysis/`, using centralized configurations to determine where code belongs and what files to share with AI.

How do I standardize repository architecture rules for outputs and configurations?▼

To standardize repository architecture, enforce centralized YAML configurations in a `configs/` directory and apply strict output conventions in an `outputs/` directory to prevent code sprawl and ensure AI models receive the correct context.

Why does my AI coding assistant generate irrelevant code changes for my project?▼

AI assistants generate irrelevant changes due to poor codebase architecture and lack of AI context management. Enforcing domain isolation and providing minimal, relevant code context guides the AI to make accurate modifications.