Skill UI

Guides engineering teams on compressing long agent conversations by focusing on tokens-per-task rather than tokens-per-request, using structured summaries, triggered strategies, and probe-based evaluation to keep artifact trails intact.

MLflow provides a framework-agnostic ML lifecycle toolkit for tracking experiments, logging models/artifacts, managing registries with versions/stages, and deploying across platforms so teams can reproduce and collaborate on PyTorch, TensorFlow, and Scikit-Learn workflows.

Automates the setup of ML experiment tracking with MLflow or W&B, configures the environment, initializes servers, and supplies snippets for logging parameters, metrics, and artifacts so runs stay reproducible and easy to compare.

Weights & Biases streamlines MLOps by logging metrics, configs, and artifacts, offering dashboards to monitor training, compare runs, optimize sweeps, and share models with teams.