Use when writing and deploying an interactive agent (e.g. a LlamaIndex program) as a served Hopsworks deployment.
Use when writing Streamlit or custom Python apps for Hopsworks, deploying from HopsFS or Git, migrating legacy apps off `APP_BASE_URL_PATH`, or managing app routing/readiness, monitoring, and public sharing. Auto-invoke when user wants to create a dashboard, deploy a Python app to Hopsworks, configure app routing/readiness, or access the feature store from an app. Input an app source + env + memory; output a running app and its URL.
Mount or ingest a table from a supported datasource. Mount tables from a datasource as an external feature group or ingest data into a new feature group using DLTHub. Auto-invoke when user works with external data (Snowflake, BigQuery, Redshift, S3, ADLS, GCS, JDBC, SQL, Databricks Unity Catalog, Postgres, MySQL, Oracle, SAP, MongoDB, CRM, REST APIs).
Use when writing Python code that creates, inserts into, or manages tables or feature groups. Auto-invoke when user writes feature pipelines, feature engineering code, or asks about feature group best practices (online vs offline, batching, OOM, materialization, embeddings, statistics).
Use when a Hopsworks job, app, or deployment needs Python libraries that are not in a base environment. Clone a base environment and install requirements or a wheel into the clone. Auto-invoke when the user hits a missing-package error in a job/app/deployment, asks to install custom dependencies, add a pip requirement, install a wheel, or pick which Python environment a workload should run in.
Use when the user asks where something lives in the Hopsworks UI or how to reach a page (Data Sources, Feature Groups, Feature Views, Model Registry, Deployments, Apps, Agents, Jobs, Jupyter, Project Settings). Knowledge skill: the project-scoped sidebar layout and what sits under each section, so you can point users to the right place.
Use when working with Hopsworks — feature groups, feature views, training datasets, storage connectors, models, deployments, projects, jobs, and datasets. Auto-invoke when the user discusses feature engineering, feature store operations, ML pipelines, model serving, external data sources, Superset, or needs to interact with Hopsworks.
Use when training an ML model. Load training data from an existing feature view, train a model with an appropriate ML framework, evaluate it, and register the model and its evaluation (metrics, plots) in the Hopsworks model registry. Input - a feature view; Output - a registered model.