skill-test

Create, run, and review Databricks skill tests via a YAML-first GRP pipeline.

1.8k|404|Updated Dec 17, 2025
One-click install
npx skills add https://github.com/databricks-solutions/ai-dev-kit --skill skill-test
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-test
Source: https://github.com/databricks-solutions/ai-dev-kit/tree/main/.test
Command: npx skills add https://github.com/databricks-solutions/ai-dev-kit --skill skill-test

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python-dotenv, PyYAML, mlflow, databricks-sdk, and includes scripts (resource) and references (resource) components.

What problem does it solve?

The Skill Testing Framework centralizes Databricks skill evaluation by providing a reproducible, YAML-first workflow for generating test scaffolding, executing tests (including optional Databricks MCP), and promoting results to ground truth for reliable regression testing.

Core Features & Use Cases

  • YAML-driven test definitions and a Generate-Review-Promote (GRP) pipeline to streamline skill validation.
  • Interactive CLI for creating test prompts, running skills, and reviewing candidates with MLflow-backed metrics.
  • Ground-truth baselines and MLflow integration to track regressions across skill versions and environments.
  • Routing and trace evaluation support to validate skill activation and Claude Code behavior.

Quick Start

  1. Install and configure the skill-test CLI in your project (pip install -e ".test/").
  2. Initialize testing scaffolding for a skill with /skill-test <skill-name> init, then run evaluations with /skill-test <skill-name> run.
  3. Use ground_truth.yaml and baseline.yaml to manage regression tests and track improvements over time.

Frequently Asked Questions about skill-test

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run regression tests for Databricks skills using YAML?▼

Run regression tests for Databricks skills by defining test cases in YAML files and executing the Generate-Review-Promote pipeline to validate behavior and promote results to ground truth baselines.

How does MLflow integration work with skill testing frameworks?▼

MLflow integration tracks evaluation metrics and trace analysis during skill testing, allowing you to monitor regressions across skill versions and environments using baseline comparisons.

Can I use Claude Code routing and trace evaluation to validate skill activation?▼

Yes, the skill testing framework supports routing and trace evaluation to validate skill activation and verify Claude Code behavior through MLflow-backed metrics and interactive CLI reviews.

What is the Generate-Review-Promote pipeline for YAML test definitions?▼

The Generate-Review-Promote pipeline is a YAML-first workflow that generates test scaffolding, executes skill tests, and promotes reviewed candidates to ground truth for reliable regression testing.

Do I need the Databricks SDK and python-dotenv to run skill tests?▼

Yes, the framework requires python-dotenv, PyYAML, mlflow, and databricks-sdk dependencies to execute tests, manage YAML configurations, and integrate with Databricks environments.

How do I initialize and execute a skill test from the CLI?▼

Initialize test scaffolding with `/skill-test <skill-name> init`, then execute evaluations using `/skill-test <skill-name> run` to generate prompts and review candidates against baselines.