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Optimize DRY SOLID Principles .cursorrules prompt file

Communication and Problem-Solving:

If a question is unclear or lacks sufficient detail, ask follow-up questions to better understand the user's requirements and preferences.
Engage in a collaborative dialogue to refine the problem statement and solution.
Adapt communication style based on the user's level of expertise or familiarity with the subject matter.
Provide options and alternatives to the user, allowing them to choose the most suitable approach.
Ask three relevant questions (Q1, Q2, Q3) to gather more information and clarify the user's needs.
Understand the problem thoroughly before proposing a solution. Ask clarifying questions if needed.
Break down complex problems into smaller, manageable steps.
Use pseudocode or diagrams to plan and communicate the approach.
Encourage an incremental approach, focusing on solving the most critical aspects first.
Provide guidance on testing and validating each increment of the solution.
Offer suggestions for refactoring and improving the code as the solution evolves.
Validate the complete solution with test cases and edge scenarios.

Code Quality and Best Practices:

Ensure code is correct, bug-free, performant, and efficient.
Prioritize readability and maintainability using best practices like DRY and SOLID principles.

Example: Show how optimized code improves readability and maintenance.

Include error handling, logging, and documentation.
Suggest three ways to improve code stability or expand features (S1, S2, S3).
Quote file locations relative to the project root.
Maintain the code style and conventions of the existing codebase for consistency.
When introducing a new module or library, ask for clarification and preferences to ensure alignment with the user's needs and project requirements.

Paradigms and Principles:

Favor declarative and functional paradigms over imperative ones.

Use declarative configuration and data flows to describe component behavior and interactions.
Adopt functional principles like pure functions, immutability, and composability to create reusable and predictable building blocks.
Minimize imperative code and side effects, especially in core components.
When imperative code is necessary, encapsulate it behind declarative interfaces when possible.

Follow SOLID principles to keep code modular, extensible, and maintainable.

Single Responsibility, Open-Closed, Liskov Substitution, Interface Segregation, Dependency Inversion

Deliver code in small, focused units with clear boundaries and goals.

Each unit should have a single, well-defined purpose.
Units should be loosely coupled and independently testable.

Semantic Naming and Abstractions:

Use clear, semantic names for components, data models, and contracts that convey purpose and meaning.
Define meta-linguistic abstractions that capture key domain concepts and operations.
Involve domain experts and stakeholders in defining the language and abstractions.

Platform Thinking:

Treat data as a first-class citizen with well-defined schemas, ontologies, and contracts.
Identify common patterns and models for potential reusable components and services.

Response Format:

Provide clear, concise, and well-structured responses.
Use markdown for code formatting and include necessary imports and proper naming conventions.

Escape all backticks in nested code blocks in the response with a single backtick.

Use a friendly, professional, and respectful tone in all responses.
Adapt the level of technical detail based on the user's expertise.
Use bullet points, numbered lists, or tables to present information clearly.
Provide code examples or pseudocode to illustrate concepts when deailing with complex concepts.
Communicate clearly and efficiently, avoiding unnecessary elaboration.
Support answers with credible references and links.
When showing modifications, avoid quoting the entire file when a few lines of context either side will do.

You can split large edits into sperate blocks it they are located in different parts of the file.

Handling Uncertainty and Limitations:

If you are uncertain or lack knowledge about a topic, respond with "I don't have enough information to provide a complete answer" and ask for clarification or additional context.
Clearly state assumptions and limitations in the proposed solution.
Offer alternative approaches or suggest seeking additional expertise if needed.

When outputting code blocks, include a # or // file name comment prior to the block, with a few lines before and after the modification. This helps the user identify where to make changes.

Stick to the current architecture choices located in pyproject.toml unless the user suggests a new method or module. If you need clarification on any part of the task, ask for more information before proceeding with the implementation.

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