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GitHub .cursorrules prompt file instructions

Jeremy Russell

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GitHub .cursorrules prompt file instructions

Writing code is like giving a speech. If you use too many big words, you confuse your audience. Define every word, and you end up putting your audience to sleep.
Similarly, when you write code, you shouldn't just focus on making it work. You should also aim to make it readable, understandable, and maintainable for future readers. To paraphrase software engineer Martin Fowler, "Anybody can write code that a computer can understand. Good programmers write code that humans can understand."
As software developers, understanding how to write clean code that is functional, easy to read, and adheres to best practices helps you create better software consistently.
This article discusses what clean code is and why it's essential and provides principles and best practices for writing clean and maintainable code.
What Is Clean Code?
Clean code is a term used to refer to code that is easy to read, understand, and maintain. It was made popular by Robert Cecil Martin, also known as Uncle Bob, who wrote "Clean Code: A Handbook of Agile Software Craftsmanship" in 2008. In this book, he presented a set of principles and best practices for writing clean code, such as using meaningful names, short functions, clear comments, and consistent formatting.
Ultimately, the goal of clean code is to create software that is not only functional but also readable, maintainable, and efficient throughout its lifecycle.
Why Is Clean Code Important?
When teams adhere to clean code principles, the code base is easier to read and navigate, which makes it faster for developers to get up to speed and start contributing. Here are some reasons why clean code is essential.
Readability and maintenance: Clean code prioritizes clarity, which makes reading, understanding, and modifying code easier. Writing readable code reduces the time required to grasp the code's functionality, leading to faster development times.

Team collaboration: Clear and consistent code facilitates communication and cooperation among team members. By adhering to established coding standards and writing readable code, developers easily understand each other's work and collaborate more effectively.

Debugging and issue resolution: Clean code is designed with clarity and simplicity, making it easier to locate and understand specific sections of the codebase. Clear structure, meaningful variable names, and well-defined functions make it easier to identify and resolve issues.

Improved quality and reliability: Clean code prioritizes following established coding standards and writing well-structured code. This reduces the risk of introducing errors, leading to higher-quality and more reliable software down the line.
Now that we understand why clean code is essential, let's delve into some best practices and principles to help you write clean code.
Principles of Clean Code
Like a beautiful painting needs the right foundation and brushstrokes, well-crafted code requires adherence to specific principles. These principles help developers write code that is clear, concise, and, ultimately, a joy to work with.
Let's dive in.
1. Avoid Hard-Coded Numbers
Use named constants instead of hard-coded values. Write constants with meaningful names that convey their purpose. This improves clarity and makes it easier to modify the code.
Example:
The example below uses the hard-coded number 0.1 to represent a 10% discount. This makes it difficult to understand the meaning of the number (without a comment) and adjust the discount rate if needed in other parts of the function.
Before:
def calculate_discount(price):
  discount = price * 0.1 # 10% discount
  return price - discount
The improved code replaces the hard-coded number with a named constant TEN_PERCENT_DISCOUNT. The name instantly conveys the meaning of the value, making the code more self-documenting.
After :
def calculate_discount(price):
TEN_PERCENT_DISCOUNT = 0.1
discount = price * TEN_PERCENT_DISCOUNT
return price - discount
Also, If the discount rate needs to be changed, it only requires modifying the constant declaration, not searching for multiple instances of the hard-coded number.
2. Use Meaningful and Descriptive Names
Choose names for variables, functions, and classes that reflect their purpose and behavior. This makes the code self-documenting and easier to understand without extensive comments.
As Robert Martin puts it, “A name should tell you why it exists, what it does, and how it is used. If a name requires a comment, then the name does not reveal its intent.”
Example:
If we take the code from the previous example, it uses generic names like "price" and "discount," which leaves their purpose ambiguous. Names like "price" and "discount" could be interpreted differently without context.
Before:
def calculate_discount(price):
TEN_PERCENT_DISCOUNT = 0.1
discount = price * TEN_PERCENT_DISCOUNT
return price - discount
Instead, you can declare the variables to be more descriptive.
After:
def calculate_discount(product_price):
  TEN_PERCENT_DISCOUNT = 0.1
  discount_amount = product_price * TEN_PERCENT_DISCOUNT
  return product_price - discount_amount
This improved code uses specific names like "product_price" and "discount_amount," providing a clearer understanding of what the variables represent and how we use them.
3. Use Comments Sparingly, and When You Do, Make Them Meaningful
You don't need to comment on obvious things. Excessive or unclear comments can clutter the codebase and become outdated, leading to confusion and a messy codebase.
Example:
Before:
def group_users_by_id(user_id):
  # This function groups users by id
  # ... complex logic ...
  # ... more code …
The comment about the function is redundant and adds no value. The function name already states that it groups users by id; there's no need for a comment stating the same.
Instead, use comments to convey the "why" behind specific actions or explain behaviors.
After:
def group_users_by_id(user_id):
  """Groups users by id to a specific category (1-9).

Warning: Certain characters might not be handled correctly.
Please refer to the documentation for supported formats.

Args:
user_id (str): The user id to be grouped.

Returns:
int: The category number (1-9) corresponding to the user id.

  Raises:
    ValueError: If the user id is invalid or unsupported.
  """
  # ... complex logic ...
  # ... more code …
This comment provides meaningful information about the function's behavior and explains unusual behavior and potential pitfalls.
4. Write Short Functions That Only Do One Thing
Follow the single responsibility principle (SRP), which means that a function should have one purpose and perform it effectively. Functions are more understandable, readable, and maintainable if they only have one job. It also makes testing them very easy.
If a function becomes too long or complex, consider breaking it into smaller, more manageable functions.
Example:
Before:
def process_data(data):
  # ... validate users...
  # ... calculate values ...
  # ... format output …
This function performs three tasks: validating users, calculating values, and formatting output. If any of these steps fail, the entire function fails, making debugging a complex issue. If we also need to change the logic of one of the tasks, we risk introducing unintended side effects in another task.
Instead, try to assign each task a function that does just one thing.
After:
def validate_user(data):
  # ... data validation logic ...
def calculate_values(data):
  # ... calculation logic based on validated data ...
def format_output(data):
  # ... format results for display …
The improved code separates the tasks into distinct functions. This results in more readable, maintainable, and testable code. Also, If a change needs to be made, it will be easier to identify and modify the specific function responsible for the desired functionality.
5. Follow the DRY (Don't Repeat Yourself) Principle and Avoid Duplicating Code or Logic
Avoid writing the same code more than once. Instead, reuse your code using functions, classes, modules, libraries, or other abstractions. This makes your code more efficient, consistent, and maintainable. It also reduces the risk of errors and bugs as you only need to modify your code in one place if you need to change or update it.
Example:
Before:
def calculate_book_price(quantity, price):
return quantity * price
def calculate_laptop_price(quantity, price):
return quantity * price
In the above example, both functions calculate the total price using the same formula. This violates the DRY principle.
We can fix this by defining a single calculate_product_price function that we use for books and laptops. This reduces code duplication and helps improve the maintenance of the codebase.
After:
def calculate_product_price(product_quantity, product_price):
return product_quantity * product_price
6. Follow Established Code-Writing Standards
Know your programming language's conventions in terms of spacing, comments, and naming. Most programming languages have community-accepted coding standards and style guides, for example, PEP 8 for Python and Google JavaScript Style Guide for JavaScript.
Here are some specific examples:
Java:
Use camelCase for variable, function, and class names.
Indent code with four spaces.
Put opening braces on the same line.
Python:
Use snake_case for variable, function, and class names.
Use spaces over tabs for indentation.
Put opening braces on the same line as the function or class declaration.
JavaScript:
Use camelCase for variable and function names.
Use snake_case for object properties.
Indent code with two spaces.
Put opening braces on the same line as the function or class declaration.
Also, consider extending some of these standards by creating internal coding rules for your organization. This can contain information on creating and naming folders or describing function names within your organization.
7. Encapsulate Nested Conditionals into Functions
One way to improve the readability and clarity of functions is to encapsulate nested if/else statements into other functions. Encapsulating such logic into a function with a descriptive name clarifies its purpose and simplifies code comprehension. In some cases, it also makes it easier to reuse, modify, and test the logic without affecting the rest of the function.
In the code sample below, the discount logic is nested within the calculate_product_discount function, making it difficult to understand at a glance.
Example:
Before:
def calculate_product_discount(product_price):
discount_amount = 0
if product_price > 100:
  discount_amount = product_price * 0.1
elif price > 50:
  discount_amount = product_price * 0.05
else:
  discount_amount = 0
final_product_price = product_price - discount_amount
return final_product_price
We can clean this code up by separating the nested if/else condition that calculates discount logic into another function called get_discount_rate and then calling the get_discount_rate in the calculate_product_discount function. This makes it easier to read at a glance.
The get_discount_rate is now isolated and can be reused by other functions in the codebase. It’s also easier to change, test, and debug it without affecting the calculate_discount function.
After:
def calculate_discount(product_price):
discount_rate = get_discount_rate(product_price)
discount_amount = product_price * discount_rate
final_product_price = product_price - discount_amount
  return final_product_price

def get_discount_rate(product_price):
if product_price > 100:
  return 0.1
elif product_price > 50:
  return 0.05
else:
  return 0
8. Refactor Continuously
Regularly review and refactor your code to improve its structure, readability, and maintainability. Consider the readability of your code for the next person who will work on it, and always leave the codebase cleaner than you found it.
9. Use Version Control
Version control systems meticulously track every change made to your codebase, enabling you to understand the evolution of your code and revert to previous versions if needed. This creates a safety net for code refactoring and prevents accidental deletions or overwrites.
Use version control systems like GitHub, GitLab, and Bitbucket to track changes to your codebase and collaborate effectively with others.

stack:

GitHub

GitLab

Bitbucket

GitHub .cursorrules prompt file instructions

Jeremy Russell

About .cursorrules prompt file

What you can build

Code Readability and Maintainability Analyzer: A tool that inspects existing codebases and provides feedback on readability and maintainability by highlighting areas for improvement, such as complex logic, hard-coded numbers, or non-descriptive variable names.
Variable Name Suggestion Tool: An app that suggests meaningful and descriptive variable, function, and class names based on their context and usage to help developers write self-documenting code.
DRY Principle Checker: A service that scans codebases to find duplicate code and suggests ways to refactor the code to adhere to the Don't Repeat Yourself (DRY) principle.
Nested Conditional Refactoring Tool: A tool that detects nested conditional statements and suggests ways to encapsulate them into separate functions for better readability and maintainability.
Comments Quality Analyzer: An application that analyzes code comments to identify unnecessary or outdated comments and suggest improvements for meaningful commentary.
Continuous Refactoring Support Tool: An integrated development tool that provides continuous feedback on potential areas for refactoring as developers write and modify code.
Coding Standards Enforcer: A plugin or service that automatically checks code against established coding standards and guidelines for specific languages, providing immediate feedback to developers.
Version Control Best Practices Guide: An online resource or tool that provides guidance and tutorials on best practices for using version control systems, especially for collaborative work and refactoring projects.
Function Complexity Minimizer: A tool designed to break down overly complex functions into smaller, single-responsibility functions, making it easier for developers to maintain their code.
Code Style Guide Generator: A service that helps teams create customized coding style guides based on their unique requirements while adhering to general language conventions.

Benefits

Emphasizes the importance of readability and maintainability, following Martin Fowler and Robert C. Martin principles.
Provides practical examples and detailed practices to maintain clean code, like the DRY principle and meaningful comments.
Offers language-specific guidelines, referencing accepted standards such as PEP 8 for Python and Google JavaScript Style Guide.

Synopsis

Software developers can use this prompt to improve code quality by implementing clean code practices, enhancing readability, maintainability, and team collaboration in their projects.

Overview of .cursorrules prompt

The .cursorrules file discusses the importance of writing clean, readable, and maintainable code. It outlines the concept of "clean code" as defined by Robert Cecil Martin and describes its significance in improving code readability, maintenance, team collaboration, debugging, and code quality. The file details best practices and principles for achieving clean code, including using named constants, meaningful names, sparing but meaningful comments, short functions adhering to the single responsibility principle, avoiding code duplication, following coding standards, encapsulating nested conditionals, continuous refactoring, and using version control systems. These guidelines aim to help developers create more efficient, reliable, and understandable software.

GitHub .cursorrules prompt file instructions

GitHub .cursorrules prompt file instructions

GitHub .cursorrules prompt file instructions

About .cursorrules prompt file

What you can build

Benefits

Synopsis

Overview of .cursorrules prompt

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