Triage is a Python framework for end-to-end machine learning workflows focused on temporal prediction problems, particularly in social good domains. It provides modular components for feature generation (architect), model training/evaluation (catwalk), experiment orchestration, model selection (audition), and post-modeling analysis. The system uses PostgreSQL for storing experiment metadata, feature matrices, and model predictions, with SQLAlchemy ORM and Alembic migrations managing the schema. Key entry points include the CLI (`triage experiment`, `triage audition`) and `manage.py` for development tasks. The architecture emphasizes reproducibility through configuration-driven experiments, temporal validation to prevent lookahead bias, and metadata tracking. While powerful, the system has complexity from multiple interdependent components and external service dependencies (PostgreSQL, Redis for RQ).