databricks
7 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
databricksdatabricks-aibi-dashboards
Create Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.
- instalações
- 1
- GitHub Stars
- 358
- Atualizado
- 19 de out.
databricksdatabricks-apps
Build apps on Databricks Apps platform. Use when asked to create data apps, analytics tools, or custom interactive visualizations. A plain \"create a dashboard\" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, not this skill. Evaluates data access patterns (analytics vs Lakebase synced tables) before scaffolding. Invoke BEFORE starting implementation.
- instalações
- 1
- GitHub Stars
- 358
- Atualizado
- 19 de out.
databricksdatabricks-data-discovery
Discover, explore, and query Databricks data via Genie — the CLI equivalent of the Genie One MCP. MUST be invoked whenever the user asks to find or locate data ('what tables are in X', 'where does X live', 'which catalog/schema has Y'), answer a natural-language question about the data, or write a SQL query.
- instalações
- 1
- GitHub Stars
- 358
- Atualizado
- 19 de out.
databricksdatabricks-iceberg
Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integr
- instalações
- 1
- GitHub Stars
- 358
- Atualizado
- 19 de out.
databricksdatabricks-jobs
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.
- instalações
- 1
- GitHub Stars
- 358
- Atualizado
- 19 de out.
databricksdatabricks-metric-views
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
- instalações
- 1
- GitHub Stars
- 358
- Atualizado
- 19 de out.
databricksdatabricks-unity-catalog
Unity Catalog governance, access control, and observability. Use to grant or revoke access (GRANT/REVOKE), reason about the privilege model and ownership, set up row-level security and column masks, create external locations and storage credentials, define catalogs/schemas/tables/volumes, answer \"who can read this table\", and query system tables (audit, lineage, billing) or work with volume files in /Volumes/.
- instalações
- 1
- GitHub Stars
- 358
- Atualizado
- 19 de out.