datasciencemonkey

Deploy and query Databricks Model Serving endpoints. Use when (1) deploying MLflow models or AI agents to endpoints, (2) creating ChatAgent/ResponsesAgent agents, (3) integrating UC Functions or Vector Search tools, (4) querying deployed endpoints, (5) checking endpoint status. Covers classical ML models, custom pyfunc, and GenAI agents.

Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.

Create Databricks AI/BI dashboards. CRITICAL: You MUST test ALL SQL queries via execute_sql BEFORE deploying. Follow guidelines strictly.

Builds Python-based Databricks applications using Dash, Streamlit, Gradio, Flask, FastAPI, or Reflex. Handles OAuth authorization (app and user auth), app resources, SQL warehouse and Lakebase connectivity, model serving integration, and deployment. Use when building Python web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app.

Create and configure Databricks Asset Bundles (DABs) with best practices for multi-environment deployments. Use when working with: (1) Creating new DAB projects, (2) Adding resources (dashboards, pipelines, jobs, alerts), (3) Configuring multi-environment deployments, (4) Setting up permissions, (5) Deploying or running bundle resources

Configure Databricks profile and authenticate for Databricks Connect, Databricks CLI, and Databricks SDK.

Databricks documentation reference. Use as a lookup resource alongside other skills and MCP tools for comprehensive guidance.

Create and query Databricks Genie Spaces for natural language SQL exploration. Use when building Genie Spaces or asking questions via the Genie Conversation API.

Best practices for cross-campaign CDP disposition reasoning. Use when evaluating customer engagement decisions involving frequency caps, campaign prioritization, channel selection, and send-time optimization.

Train and deploy propensity models using Databricks AutoML. Use when the agent needs campaign-specific propensity scores and no Model Serving endpoint exists yet, or when retraining is needed for a new campaign type.

DBOS Python SDK for building reliable, fault-tolerant applications with durable workflows. Use this skill when writing Python code with DBOS, creating workflows and steps, using queues, using DBOSClient from external applications, or building applications that need to be resilient to failures.

A simple greeting skill that says hello to a user by name. Use when the user asks for a greeting or hello message.

Use when applying a brand theme configuration to the app. Reads theme_config.json and updates Dart theme files with new colors, gradients, logos, and animation effects.

Use when configuring app branding, theming, or visual styling. Guides through Q&A to capture logo, colors, and animation preferences, then generates theme_config.json.

Use when deploying BrickChat to Databricks Apps, updating an existing deployment, or troubleshooting deployment issues (project)