simplify

Name: simplify
Availability: InStock
Author: woodfishhhh

Review generated Python files for complexity and produce a simplify_report.md.

32|1|Updated May 19, 2026

Search Tags:#quality assurance #code review #refactoring #python #vectorization #modeling pipeline

Ready to run

npx skills add https://github.com/woodfishhhh/EZ_math_model --skill simplify-woodfishhhh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: simplify
Source: https://github.com/woodfishhhh/EZ_math_model/tree/main/skills/ez-math-model/tools/simplify
Command: npx skills add https://github.com/woodfishhhh/EZ_math_model --skill simplify-woodfishhhh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

simplify helps you find and eliminate unnecessary complexity in generated modeling code so your pipeline stays maintainable and behavior-consistent.

Core Features & Use Cases

Post-coding quality review: Run after pipeline 03 coding to inspect produced src/*.py before packaging.
Non-modifying recommendations: Outputs a report with suggestions only, avoiding behavioral changes during review.
Common code smells detection: Targets duplication, inefficient loops, over-defensive exception handling, unclear naming, and mismatches with modeling_plan.

Quick Start

Tell the AI to load the simplify skill, review all generated files under src/*.py, and write the quality report to workdir/{task_id}/simplify_report.md.

Frequently Asked Questions about simplify

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

FAQPage Schema

How do I review generated Python modeling code for unnecessary complexity?▼

To review generated Python modeling code for unnecessary complexity, analyze the src/*.py files for duplication, inefficient loops, and overly defensive exception handling, then generate a markdown report with non-modifying recommendations.

What is the best way to check modeling pipeline code for reuse and vectorization?▼

The best way to check modeling pipeline code for reuse and vectorization is to perform a post-coding quality review that inspects the produced src/*.py files for inefficiencies and naming inconsistencies against the modeling plan.

How does a non-modifying code review work for Python files?▼

A non-modifying code review works by analyzing the source files to identify code smells like exception swallowing and mismatches with the modeling plan, outputting a recommendation artifact without changing the actual behavior of the code.

When do I need to run a code quality review in a modeling pipeline?▼

You need to run a code quality review in a modeling pipeline after the coding stage and before packaging, ensuring the generated src/*.py files are maintainable and behavior-consistent before they are finalized.

Can I detect exception swallowing and overly defensive error handling in Python?▼

Yes, you can detect exception swallowing and overly defensive error handling in Python by analyzing the source files for these specific code smells and documenting them in a simplify report.

What limitations exist when refactoring modeling code based on automated recommendations?▼

A key limitation when refactoring modeling code based on automated recommendations is that the review process itself outputs suggestions only and does not change code behavior, requiring you to apply the refactoring manually.