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clean-code

Enforce pragmatic coding standards for AI agents with pre-edit checks and self-verification.

Updated Jan 20, 2026
Search Tags:#code quality#refactoring#clean code#coding standards#AI coding#pragmatic programming
One-click install
npx skills add https://github.com/yunaamelia/mcp-agent-memory-pro --skill clean-code-yunaamelia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clean-code
Source: https://github.com/yunaamelia/mcp-agent-memory-pro/tree/main/.agent/skills/clean-code
Command: npx skills add https://github.com/yunaamelia/mcp-agent-memory-pro --skill clean-code-yunaamelia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, and includes scripts (resource) components.

What problem does it solve?

This Skill enforces pragmatic coding standards to ensure code is concise, direct, and avoids over-engineering or unnecessary comments, leading to more maintainable and understandable software.

Core Features & Use Cases

  • Enforces SOLID principles: Promotes Single Responsibility, Don't Repeat Yourself, Keep It Simple, You Aren't Gonna Need It, and the Boy Scout Rule.
  • Standardizes Naming Conventions: Ensures clear and intent-revealing names for variables, functions, and constants.
  • Optimizes Function Design: Guides developers to write small, focused functions with minimal arguments and no side effects.
  • Promotes Clean Structure: Encourages guard clauses, flat code, and composition over deep nesting.
  • AI Coding Style Guidelines: Provides specific instructions for AI agents on how to handle feature requests, bug reports, and unclear requirements.
  • Pre-edit Checks: Mandates a "think first" approach before editing any file, considering imports, tests, and shared components.
  • Post-completion Verification: Requires a mandatory self-check before marking a task as complete, ensuring goals are met, code works, and no errors are left.
  • Script Output Handling: Defines a strict protocol for running, parsing, summarizing, and acting upon validation script outputs.

Quick Start

Use the clean-code skill to refactor the provided code snippet, ensuring it adheres to the Single Responsibility Principle and has clear variable names.

Frequently Asked Questions about clean-code

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

FAQPage Schema
How do I enforce clean code standards when refactoring Python code?▼

To enforce clean code standards, apply principles like SRP, DRY, KISS, and YAGNI to ensure concise, direct, and solution-oriented code. Use pre-editing checks to consider imports and tests before modifying files.

What are the best coding standards for AI agents to write maintainable Python?▼

The best coding standards for AI agents mandate strict naming conventions, minimal function arguments, and guard clauses. AI agents must follow a defined process for handling validation script outputs and perform mandatory self-verification before task completion.

How does pragmatic programming prevent over-engineering in software development?▼

Pragmatic programming prevents over-engineering by enforcing the You Aren't Gonna Need It (YAGNI) principle and keeping code simple (KISS). This approach focuses on direct solutions and avoids unnecessary comments or complex nesting.

Do I need Python to use coding standards skills for AI code generation?▼

Yes, Python is required as a dependency to use this coding standards skill. It runs scripts to parse, summarize, and act upon validation outputs, ensuring code quality and safety through post-completion verification.

Why should I use the Boy Scout Rule for code quality improvements?▼

You should use the Boy Scout Rule for code quality because it mandates leaving code cleaner than you found it. Combined with composition over deep nesting and flat code structures, it ensures long-term maintainability and readability.

What is the process for handling validation script outputs in AI coding?▼

The process for handling validation script outputs in AI coding involves running, parsing, summarizing, and acting upon the results. This strict protocol ensures code quality and safety by catching errors before marking a task complete.

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