databricks-solutions/ai-dev-kit

skill-test

Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill",…

원문 언어: 영어

tool-selection

Evaluates whether the agent selected appropriate MCP tools instead of shell workarounds. Load when the trace contains Bash tool calls that could have used MCP tools, or when evaluating tool call efficiency.

원문 언어: 영어

databricks-python-sdk

Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.

원문 언어: 영어

general-quality

General response quality evaluation. Always applicable regardless of domain. Covers response structure, actionability, clarity, and hallucination detection.

원문 언어: 영어

sql-correctness

SQL evaluation criteria for Databricks. Load when the trace contains execute_sql tool calls or SQL code in responses. Covers syntax validity, Unity Catalog patterns, and Databricks-specific SQL features.

원문 언어: 영어

python-dev

Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).

원문 언어: 영어