AGENT SKILLS · ミッション装備 / databricks

databricks · Agent Skills

7 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.

databricks

コミュニティ

databricks-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.

導入数: 1
GitHub Stars: 358
更新日: 10月19日

databricks-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.

導入数: 1
GitHub Stars: 358
更新日: 10月19日

databricks-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.

導入数: 1
GitHub Stars: 358
更新日: 10月19日

databricks-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

導入数: 1
GitHub Stars: 358
更新日: 10月19日

databricks-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.

導入数: 1
GitHub Stars: 358
更新日: 10月19日

databricks-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.

導入数: 1
GitHub Stars: 358
更新日: 10月19日

databricks-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/.

導入数: 1
GitHub Stars: 358
更新日: 10月19日

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