Create and query Databricks Genie Spaces for natural language SQL exploration. Use when building Genie Spaces or asking questions via the Genie Conversation API.
How this skill is triggered — by the user, by Claude, or both
Slash command
/databricks-ai-dev-kit:databricks-genieThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration.
Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration.
Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally.
Use this skill when:
| Tool | Purpose |
|---|---|
create_or_update_genie | Create or update a Genie Space |
get_genie | Get space details (by ID) or list all spaces (no ID) |
delete_genie | Delete a Genie Space |
| Tool | Purpose |
|---|---|
ask_genie | Ask a question or follow-up (conversation_id optional) |
| Tool | Purpose |
|---|---|
get_table_details | Inspect table schemas before creating a space |
execute_sql | Test SQL queries directly |
Before creating a Genie Space, understand your data:
get_table_details(
catalog="my_catalog",
schema="sales",
table_stat_level="SIMPLE"
)
create_or_update_genie(
display_name="Sales Analytics",
table_identifiers=[
"my_catalog.sales.customers",
"my_catalog.sales.orders"
],
description="Explore sales data with natural language",
sample_questions=[
"What were total sales last month?",
"Who are our top 10 customers?"
]
)
ask_genie(
space_id="your_space_id",
question="What were total sales last month?"
)
# Returns: SQL, columns, data, row_count
1. Inspect tables → get_table_details
2. Create space → create_or_update_genie
3. Query space → ask_genie (or test in Databricks UI)
4. Curate (optional) → Use Databricks UI to add instructions
Before creating a Genie Space:
Use these skills in sequence:
databricks-synthetic-data-generation - Generate raw parquet filesdatabricks-spark-declarative-pipelines - Create bronze/silver/gold tables| Issue | Solution |
|---|---|
| No warehouse available | Create a SQL warehouse or provide warehouse_id explicitly |
| Poor query generation | Add instructions and sample questions that reference actual column names |
| Slow queries | Ensure warehouse is running; use OPTIMIZE on tables |
npx claudepluginhub vkn129/ai-dev-kit --plugin databricks-ai-dev-kitCreates structured, bite-sized implementation plans from specs or requirements before writing code. Useful for breaking down multi-step tasks into testable steps with file structure and task boundaries.
2plugins reuse this skill
First indexed Jul 13, 2026