ML Adoption Playbook This skill provides an adaptive methodology for implementing machine learning models into existing software engineering projects. It bridges the gap between traditional SWE and MLOps by structuring how ML should be researched, decoupled, trained, and integrated. When to Activate - A user asks to "add ML" or "add an algorithm" to their existing codebase. - Planning the integration of a new model (e.g., recommendation, classification, forecasting) into a non-ML application. - Structuring a workflow for an agent to build, train, and deploy an ML component adaptively. Phase 1…