What problem does it solve? Setting up Databricks projects from an external coding harness involves fragmented tooling, ad-hoc authentication, and manual deployment steps that are hard to reproduce and validate. This Skill standardizes the integration of the Databricks CLI, Unified Authentication, Declarative Automation Bundles, and selected AI Dev Kit components into a reproducible, auditable workflow. ## Core Features & Use Cases - Reproducible project setup: Pin the Databricks CLI and AI Dev Kit versions, verify authentication with read-only commands, and record selected components before any deployment. - Declarative deployment: Express deployable resources as Declarative Automation Bundles and validate them with databricks bundle validate before deploying to an approved development target. - Evidence-driven validation: Execute a seeded golden path and collect resource, run, trace, evaluation, and cost evidence, with documented teardown requiring explicit approval. - Use Case: A field engineer building a Databricks demo or POC uses this Skill to authenticate securely, bundle the project resources, validate the target, deploy to a dev workspace, and gather run and cost evidence for stakeholder review. ## Quick Start Set up and validate a Databricks AI project using the AI Dev Kit workflow, including CLI verification, bundle validation, and a golden-path deployment to a development target.
