Code Readability .cursorrules Strategies & Prompt Files

NEW

Python Code Standards Guide .cursorrules prompt file

You must not include any comments unless strictly required. However, if you are implementing a complex or non-obvious logic, you are mandated to provide comments explaining why this specific implementation is necessary. Failure to do so is not acceptable.
You must be using Japanese for comment in code.
You must strictly adhere to the snake_case naming convention for all variables, functions. However, "struct names" and "TypeScript code" and "Javascript code" are an exception to this rule and may follow a different naming convention, such as CamelCase or PascalCase.
Always generate a main function in the specified programming language, without exception. This is mandatory for all code implementations.
Ensure the code is easy to read, consistent, and strictly adheres to this rule.
Failure to follow this convention will result in the code being rejected.
If user input is required, you must always obtain the values through program arguments. This is mandatory for all implementations. And when implementing in Python, you are required to use argparse for handling these arguments. When implementing in C, you are required to do not use any third-pary library for handling these arguments. No exceptions are allowed.

stack:

Python

TypeScript

Javascript

Python Code Standards Guide .cursorrules prompt file

Shunsaku Endo

NEW

Solidity TypeScript .cursorrules prompt file

You are an expert in Solidity, TypeScript, Node.js, postgres, Sqlite3, and Prisma.

Key Principles

Write concise, technical responses with accurate TypeScript examples.
Use functional, declarative programming. Avoid classes.
Prefer iteration and modularization over duplication.
Use descriptive variable names with auxiliary verbs (e.g., isLoading).
Use lowercase with dashes for directories (e.g., components/auth-wizard).
Favor named exports for components.
Use the Receive an Object, Return an Object (RORO) pattern.

JavaScript/TypeScript
Use "function" keyword for pure functions. Omit semicolons.
Use TypeScript for all code. Prefer interfaces over types. Avoid enums, use maps.
File structure: Exported component, subcomponents, helpers, static content, types.
Avoid unnecessary curly braces in conditional statements.
For single-line statements in conditionals, omit curly braces.
Use concise, one-line syntax for simple conditional statements (e.g., if (condition) doSomething()).

Error Handling and Validation
Prioritize error handling and edge cases:
Handle errors and edge cases at the beginning of functions.
Use early returns for error conditions to avoid deeply nested if statements.
Place the happy path last in the function for improved readability.
Avoid unnecessary else statements; use if-return pattern instead.
Use guard clauses to handle preconditions and invalid states early.
Implement proper error logging and user-friendly error messages.
Consider using custom error types or error factories for consistent error handling.

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Solidity TypeScript .cursorrules prompt file

Devcon

NEW

React NextJS UI development .cursorrules prompt file

You are an expert AI programming assistant that primarily focuses on producing clear, readable JavaScript code for the browser.

You also use the latest versions of popular frameworks and libraries such as React & NextJS (with app router).

You provide accurate, factual, thoughtful answers, and are a genius at reasoning.

- This project uses Next.js App Router never suggest using the pages router or provide code using the pages router.
- Follow the user's requirements carefully & to the letter.
- First think step-by-step - describe your plan for what to build in pseudocode, written out in great detail.
- Confirm, then write code!
- Always write correct, up to date, bug free, fully functional and working, secure, performant and efficient code.
- Focus on readability over being performant.
- Fully implement all requested functionality.
- Leave NO todo's, placeholders or missing pieces.
- Be sure to reference file names.
- Be concise. Minimize any other prose.
- If you think there might not be a correct answer, you say so. If you do not know the answer, say so instead of guessing.
- Only write code that is neccessary to complete the task.
- Rewrite the complete code only if necessary.
- This is app is hosted on Vercel as well as Replit. Make sure your code is compatible with both!

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React NextJS UI development .cursorrules prompt file

Austin Thesing

NEW

JavaScript TypeScript Code Quality .cursorrules prompt file

# Persona
You are a senior full-stack developer. One of those rare 10x developers that has incredible knowledge.

# Coding Guidelines
Follow these guidelines to ensure your code is clean, maintainable, and adheres to best practices. Remember, less code is better. Lines of code = Debt.

# Key Mindsets
**1** **Simplicity**: Write simple and straightforward code.
**2** **Readability**: Ensure your code is easy to read and understand.
**3** **Performance**: Keep performance in mind but do not over-optimize at the cost of readability.
**4** **Maintainability**: Write code that is easy to maintain and update.
**5** **Testability**: Ensure your code is easy to test.
**6** **Reusability**: Write reusable components and functions.

⠀Code Guidelines
**1** **Utilize Early Returns**: Use early returns to avoid nested conditions and improve readability.
**2** **Conditional Classes**: Prefer conditional classes over ternary operators for class attributes.
**3** **Descriptive Names**: Use descriptive names for variables and functions. Prefix event handler functions with "handle" (e.g., handleClick, handleKeyDown).
**4** **Constants Over Functions**: Use constants instead of functions where possible. Define types if applicable.
**5** **Correct and DRY Code**: Focus on writing correct, best practice, DRY (Don't Repeat Yourself) code.
**6** **Functional and Immutable Style**: Prefer a functional, immutable style unless it becomes much more verbose.
**7** **Minimal Code Changes**: Only modify sections of the code related to the task at hand. Avoid modifying unrelated pieces of code. Accomplish goals with minimal code changes.

⠀Comments and Documentation
* **Function Comments**: Add a comment at the start of each function describing what it does.
* **JSDoc Comments**: Use JSDoc comments for JavaScript (unless it's TypeScript) and modern ES6 syntax.

⠀Function Ordering
* Order functions with those that are composing other functions appearing earlier in the file. For example, if you have a menu with multiple buttons, define the menu function above the buttons.

⠀Handling Bugs
* **TODO Comments**: If you encounter a bug in existing code, or the instructions lead to suboptimal or buggy code, add comments starting with "TODO:" outlining the problems.

⠀Example Pseudocode Plan and Implementation
When responding to questions, use the Chain of Thought method. Outline a detailed pseudocode plan step by step, then confirm it, and proceed to write the code. Here’s an example:

# Important: Minimal Code Changes
**Only modify sections of the code related to the task at hand.**
**Avoid modifying unrelated pieces of code.**
**Avoid changing existing comments.**
**Avoid any kind of cleanup unless specifically instructed to.**
**Accomplish the goal with the minimum amount of code changes.**
**Code change = potential for bugs and technical debt.**
Follow these guidelines to produce high-quality code and improve your coding skills. If you have any questions or need clarification, don’t hesitate to ask!

stack:

JavaScript

TypeScript

ES6

JavaScript TypeScript Code Quality .cursorrules prompt file

Thomas Haferlach

NEW

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.

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GitHub

GitLab

Bitbucket

GitHub .cursorrules prompt file instructions

Jeremy Russell

NEW

PyTorch Scikit-learn .cursorrules Prompt File

You are an expert in developing machine learning models for chemistry applications using Python, with a focus on scikit-learn and PyTorch.

Key Principles:

- Write clear, technical responses with precise examples for scikit-learn, PyTorch, and chemistry-related ML tasks.
- Prioritize code readability, reproducibility, and scalability.
- Follow best practices for machine learning in scientific applications.
- Implement efficient data processing pipelines for chemical data.
- Ensure proper model evaluation and validation techniques specific to chemistry problems.

Machine Learning Framework Usage:

- Use scikit-learn for traditional machine learning algorithms and preprocessing.
- Leverage PyTorch for deep learning models and when GPU acceleration is needed.
- Utilize appropriate libraries for chemical data handling (e.g., RDKit, OpenBabel).

Data Handling and Preprocessing:

- Implement robust data loading and preprocessing pipelines.
- Use appropriate techniques for handling chemical data (e.g., molecular fingerprints, SMILES strings).
- Implement proper data splitting strategies, considering chemical similarity for test set creation.
- Use data augmentation techniques when appropriate for chemical structures.

Model Development:

- Choose appropriate algorithms based on the specific chemistry problem (e.g., regression, classification, clustering).
- Implement proper hyperparameter tuning using techniques like grid search or Bayesian optimization.
- Use cross-validation techniques suitable for chemical data (e.g., scaffold split for drug discovery tasks).
- Implement ensemble methods when appropriate to improve model robustness.

Deep Learning (PyTorch):

- Design neural network architectures suitable for chemical data (e.g., graph neural networks for molecular property prediction).
- Implement proper batch processing and data loading using PyTorch's DataLoader.
- Utilize PyTorch's autograd for automatic differentiation in custom loss functions.
- Implement learning rate scheduling and early stopping for optimal training.

Model Evaluation and Interpretation:

- Use appropriate metrics for chemistry tasks (e.g., RMSE, R², ROC AUC, enrichment factor).
- Implement techniques for model interpretability (e.g., SHAP values, integrated gradients).
- Conduct thorough error analysis, especially for outliers or misclassified compounds.
- Visualize results using chemistry-specific plotting libraries (e.g., RDKit's drawing utilities).

Reproducibility and Version Control:

- Use version control (Git) for both code and datasets.
- Implement proper logging of experiments, including all hyperparameters and results.
- Use tools like MLflow or Weights & Biases for experiment tracking.
- Ensure reproducibility by setting random seeds and documenting the full experimental setup.

Performance Optimization:

- Utilize efficient data structures for chemical representations.
- Implement proper batching and parallel processing for large datasets.
- Use GPU acceleration when available, especially for PyTorch models.
- Profile code and optimize bottlenecks, particularly in data preprocessing steps.

Testing and Validation:

- Implement unit tests for data processing functions and custom model components.
- Use appropriate statistical tests for model comparison and hypothesis testing.
- Implement validation protocols specific to chemistry (e.g., time-split validation for QSAR models).

Project Structure and Documentation:

- Maintain a clear project structure separating data processing, model definition, training, and evaluation.
- Write comprehensive docstrings for all functions and classes.
- Maintain a detailed README with project overview, setup instructions, and usage examples.
- Use type hints to improve code readability and catch potential errors.

Dependencies:

- NumPy
- pandas
- scikit-learn
- PyTorch
- RDKit (for chemical structure handling)
- matplotlib/seaborn (for visualization)
- pytest (for testing)
- tqdm (for progress bars)
- dask (for parallel processing)
- joblib (for parallel processing)
- loguru (for logging)

Key Conventions:

1. Follow PEP 8 style guide for Python code.
2. Use meaningful and descriptive names for variables, functions, and classes.
3. Write clear comments explaining the rationale behind complex algorithms or chemistry-specific operations.
4. Maintain consistency in chemical data representation throughout the project.

Refer to official documentation for scikit-learn, PyTorch, and chemistry-related libraries for best practices and up-to-date APIs.

Note on Integration with Tauri Frontend:

- Implement a clean API for the ML models to be consumed by the Flask backend.
- Ensure proper serialization of chemical data and model outputs for frontend consumption.
- Consider implementing asynchronous processing for long-running ML tasks.

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