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decomplect-py

Analyze Python code for architectural simplicity and coupling issues.

20|5|Updated Nov 18, 2025
Search Tags:#architecture#code analysis#refactoring#design patterns#python#simplicity
One-click install
npx skills add https://github.com/zby/llm-do --skill decomplect-py
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: decomplect-py
Source: https://github.com/zby/llm-do/tree/main/arscontexta/skills/decomplect-py
Command: npx skills add https://github.com/zby/llm-do --skill decomplect-py

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill identifies architectural flaws in Python code, helping developers write simpler, more maintainable, and robust software.

Core Features & Use Cases

  • Architectural Analysis: Evaluates Python code against principles of simplicity, functional core/imperative shell, and coupling.
  • Design Review: Provides actionable feedback for refactoring and improving code quality.
  • Use Case: A team is about to merge a large refactor. They use this Skill to ensure the new code adheres to best practices and doesn't introduce new architectural debt.

Quick Start

Run decomplect-py on the current Python code changes to get an architectural assessment.

Frequently Asked Questions about decomplect-py

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

FAQPage Schema
How do I analyze Python code for architectural simplicity and coupling?▼

Python architectural analysis evaluates code against Rich Hickey's simplicity principles and functional core/imperative shell separation. It identifies complected concerns, mixed I/O logic, and module coupling to provide actionable refactoring feedback.

What is functional core and imperative shell separation in Python refactoring?▼

Functional core and imperative shell (FCIS) separation isolates pure logic from side effects. Analyzing Python code for FCIS identifies mixed I/O logic and complected concerns, ensuring maintainability and guiding refactoring.

How do I check my Python code for complected concerns before a merge?▼

Checking Python code for complected concerns involves running architectural analysis to detect tangled logic and dependency issues. This pre-refactoring assessment ensures changes adhere to best practices and prevents architectural debt.

Can I use this for a design review of existing Python module dependencies?▼

Yes, architectural analysis supports design reviews of existing Python module dependencies. It evaluates module coupling and identifies I/O mixed with logic, providing actionable feedback to improve code quality.

Why does my Python code have high coupling and mixed I/O logic?▼

High coupling and mixed I/O logic occur when architectural concerns are complected. Analyzing the codebase against simplicity principles identifies these dependency issues and separates the functional core from the imperative shell.

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