Marco Fernandes enhanced error classification for Anthropic context window overflows in the phidatahq/phidata repository. He developed a backend feature that introduced two new error-patterns, enabling the system to accurately detect and handle context overflow scenarios rather than defaulting to generic errors. Using Python, Marco integrated these patterns into the existing error-handling flow, ensuring that on_context_overflow events are managed consistently across Anthropic models. He also implemented comprehensive unit tests to validate the new classification logic, improving reliability and user-facing error messaging for long-context deployments. The work demonstrated depth in backend development, error handling, and rigorous unit testing practices.