القائمة
Create and query Databricks Genie Spaces for natural language SQL exploration. Use when building Genie Spaces or asking questions via the Genie Conversation API.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | databricks-genie |
| description | Create and query Databricks Genie Spaces for natural language SQL exploration. Use when building Genie Spaces or asking questions via the Genie Conversation API. |
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 |
|---|---|
list_genie | List all Genie Spaces accessible to you |
create_or_update_genie | Create or update a Genie Space |
get_genie | Get Genie Space details |
delete_genie | Delete a Genie Space |
| Tool | Purpose |
|---|---|
ask_genie | Ask a question to a Genie Space, get SQL + results |
ask_genie_followup | Ask follow-up question in existing conversation |
| 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 |
This skill should be used when the user asks to "write Gherkin", "create feature files", "generate BDD scenarios", "write acceptance tests in Gherkin", "create Behave features", "write Given When Then tests", "BDD test cases for my pipeline", "Gherkin for Unity Catalog", or wants to translate requirements into Gherkin feature files for Databricks.
This skill should be used when the user asks to "run BDD tests", "execute Behave", "run Gherkin tests", "run my feature files", "behave test results", "run smoke tests", "BDD test report", or needs to execute Behave test suites with specific options like tag filtering, parallel execution, or CI reporting.
This skill should be used when the user asks to "set up BDD", "create a Behave project", "scaffold BDD tests", "initialize Behave", "add BDD to my project", "set up Gherkin testing", "create test structure for Behave", or mentions setting up behavior-driven development testing. Generates a complete Behave project structure wired to Databricks SDK.
This skill should be used when the user asks to "write step definitions", "implement BDD steps", "generate step code", "create Behave steps", "implement Given When Then", "write Python steps for Gherkin", "step definitions for Databricks", or needs to create Python step implementations for existing Gherkin feature files.
Deploy and query Databricks Model Serving endpoints. Use when (1) deploying MLflow models or AI agents to endpoints, (2) creating ChatAgent/ResponsesAgent agents, (3) integrating UC Functions or Vector Search tools, (4) querying deployed endpoints, (5) checking endpoint status. Covers classical ML models, custom pyfunc, and GenAI agents.
Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.