90% of Claude-linked output going to GitHub repos w <2 stars

1. 1
   Acknowledge the Adoption Gap

   Recognize that AI-generated code often struggles with real-world adoption, as evidenced by its prevalence in low-star GitHub repositories. Understand this isn't just about correctness, but practical utility and community acceptance.
2. 2
   Evaluate Beyond Basic Functionality

   Shift your evaluation metrics for AI-generated code. Move past simple 'does it work?' to 'is it maintainable, well-documented, secure, and aligned with project standards?'
3. 3
   Prioritize Quality Attributes in Prompts

   When prompting AI code assistants, explicitly request code that adheres to maintainability, readability, documentation standards (e.g., docstrings, comments), and community best practices (e.g., PEP 8 for Python).
4. 4
   Implement Robust Human-in-the-Loop Validation

   Integrate human review and testing into your AI code generation workflow. Developers should critically review AI output for practical value, not just correctness, before integration.
5. 5
   Test in Real-World Scenarios

   Validate AI-generated code within your actual project environments. Focus on how well it integrates, performs under load, and contributes to the overall project quality, aiming for outcomes that lead to higher adoption and positive community feedback.

# Prompt for an AI code assistant to generate high-quality code.
# Focus on clarity, maintainability, and community standards.

"Generate a Python function that calculates the Nth Fibonacci number.
The function should include:
1. A clear docstring explaining its purpose, arguments, and return value.
2. Inline comments for complex logic.
3. Error handling for invalid input (e.g., non-integer or negative N).
4. Adherence to PEP 8 style guidelines.
5. A simple example usage demonstrating the function."

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