Research ML techniques, architectures, or papers relevant to the current task and summarize actionable insights.
Autonomous build loop — implements, tests, reviews, analyzes, and refines. Default 6 iterations (thorough) or --fast for 4 iterations (ship it quick).
Initialize or update a project with Databricks ML workflow scaffolding, skills, and agents. Use when starting a new ML project or adding missing Databricks integration to an…
Run a quick training experiment locally (CPU or MPS). Use when the user wants to test a model change, debug training code, run a smoke test, or validate a new architecture before…
Compare MLflow experiment runs, rank them, and recommend next steps. Use when the user asks about results, which run is best, what improved, what hurt performance, or wants a…
Submit ML training to Databricks and pull results. Use when the user wants to run, train, or execute a model on Databricks.
Submit a training job to the Databricks GPU cluster, wait for results, and pull MLflow metrics back locally.
Analyze MLflow experiment results. Use when the user asks about model performance or experiment history.