Python's software quality tooling is evolving with both rapid consolidation and deep specialization. Ruff has successfully unified many linters and formatters, providing a high-performance standard for code style. However, other essential tools remain in isolated silos, focusing on architecture and structural metrics. These specialized tools like import-linter, tach, and complexity calculators address code maintainability and architectural debt beyond simple linting. Ruff's success now allows for a broader discussion about code structure, not just syntax. Specialized tools are crucial for identifying and mitigating technical risks, preventing decay, and reducing maintenance costs. Metrics from complexity and cohesion tools act as early warning systems for technical debt. Combining data from these tools can create multi-dimensional risk scores, highlighting problematic code components. This predictive risk assessment, validated against historical data, can prioritize refactoring efforts. Connecting maintainers of these diverse tools can increase their visibility and sustainability. A shared discussion can foster broader adoption and attract new contributors, ensuring the longevity of these valuable projects.