Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation.
Quickly install databricks-pipelines AI skill to your development environment via command line
Source: databricks/databricks-agent-skills.
FIRST: Use the parent databricks skill for CLI basics, authentication, profile selection, and data discovery commands.
Use this tree to determine which dataset type and features to use. Multiple features can apply to the same dataset — e.g., a Streaming Table can use Auto Loader for ingestion, Append Flows for fan-in, and Expectations for data quality. Choose the dataset type first, then layer on applicable features.
Pipelines use a default catalog and schema configured in the pipeline settings. All datasets are published there unless overridden.
Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation. Source: databricks/databricks-agent-skills.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/databricks/databricks-agent-skills --skill databricks-pipelinesDevelop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL. Invoke BEFORE starting implementation. Source: databricks/databricks-agent-skills.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/databricks/databricks-agent-skills --skill databricks-pipelines Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/databricks/databricks-agent-skills