simplify

Refactor Python code after tests pass to improve readability and maintainability.

2.5k|421|Updated Oct 1, 2025
One-click install
npx skills add https://github.com/meta-pytorch/OpenEnv --skill simplify
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: simplify
Source: https://github.com/meta-pytorch/OpenEnv/tree/main/.claude/skills/simplify
Command: npx skills add https://github.com/meta-pytorch/OpenEnv --skill simplify

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Refactor and clean up code after tests pass to improve readability, maintainability, and consistency, while preserving existing behavior.

Core Features & Use Cases

  • Identify refactor opportunities based on test outcomes and code smells
  • Rename variables for clarity, extract helpers, remove duplication, and simplify complex conditionals
  • Use case: after a green test suite, apply targeted edits to a module and re-run tests to ensure no regressions

Quick Start

Use the simplify skill to clean up a module after confirming tests pass, then run the test suite to verify everything remains green.

Frequently Asked Questions about simplify

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

FAQPage Schema
How do I refactor Python code while keeping tests passing?

Refactor code after confirming your test suite passes green. Apply incremental changes—rename variables for clarity, extract helpers, remove duplication—then re-run tests to verify no regressions. This post-green-phase cleanup improves readability and maintainability while preserving behavior.

What's the best way to remove code duplication in Python modules?

After tests pass, identify duplication and dead code by analyzing test outcomes and code smells. Extract repeated logic into helpers, simplify complex conditionals, and rename unclear variables. Re-test after each change to catch regressions early.

When should I refactor after tests pass instead of before?

Refactor after tests pass to establish a green baseline and reduce risk of breaking changes. Starting with passing tests lets you apply targeted edits—variable renames, helper extraction, duplication removal—with confidence that re-testing will catch any regressions immediately.

Can I refactor utilities and helper functions in Python projects?

Yes. The refactoring process applies to modules, utilities, and helpers across Python projects. Run tests first to establish a baseline, then clean up the code incrementally—removing dead code, improving names, extracting repeated logic—and re-run tests to confirm no behavior changes.

What code smells should I look for when refactoring Python?

Look for duplication, dead code, unclear variable names, and overly complex conditionals. After tests pass, refactor these issues incrementally by renaming for clarity, extracting helpers to reduce duplication, and simplifying logic. Re-test after each change to ensure behavior stays intact.