skills.rest
TRRaveendra
TRRaveendraCOMMUNITY
Explore all skills by this author →
GitHub

sdlc-agent-11-unit-test

Generates and executes PyTest suites with coverage reports for reviewed PySpark and SQL code.

1|2|Updated Aug 10, 2026
Search Tags:#unit testing#pytest#pyspark#databricks#coverage report#sdlc pipeline#foundation model api
One-click install
npx skills add https://github.com/TRRaveendra/AI-Agents-Databricks --skill sdlc-agent-11-unit-test-trraveendra
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sdlc-agent-11-unit-test
Source: https://github.com/TRRaveendra/AI-Agents-Databricks/tree/main/ai-agent-skills/databricks-sdlc-agent-skills/11-unit-test-agent
Command: npx skills add https://github.com/TRRaveendra/AI-Agents-Databricks --skill sdlc-agent-11-unit-test-trraveendra

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires databricks-sdk, and includes references (resource) components.

What problem does it solve? Manually writing unit tests for PySpark and SQL transformations in a Databricks pipeline is slow and often skipped, leaving data pipelines without verified test coverage or real pass/fail evidence. ## Core Features & Use Cases - PyTest Generation: Creates test cases covering happy paths, edge cases, negative cases, and mock data using chispa, pytest-spark, and local SparkSession fixtures. - Real Test Execution: Runs tests via the Databricks Jobs API or serverless notebooks and captures actual pass/fail results and coverage, never fabricated numbers. - Contract-Validated Output: Returns JSON matching a strict output contract and writes artifacts (tests/*.py, coverage_report.md) to Unity Catalog Volumes for the next pipeline agent. - Use Case: As stage 11 of a 15-agent SDLC pipeline, it consumes reviewed PySpark/SQL from the Code Review Agent and hands a validated test suite plus coverage report to the Testing Agent. ## Quick Start Generate and execute a PyTest suite with a coverage report for the reviewed PySpark code from ticket {ticket} and write the artifacts to the lakehouse.

Frequently Asked Questions about sdlc-agent-11-unit-test

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

FAQPage Schema
How do I generate PyTest tests for PySpark code in Databricks?▼

This agent drafts PyTest cases via the Databricks Foundation Model API covering happy paths, edge cases, and negative cases, using chispa, pytest-spark, and local SparkSession fixtures. It then executes them through the Jobs API and records real pass/fail results.

How to measure test coverage for PySpark pipelines?▼

The agent executes the generated test suite as a Databricks job run or serverless notebook and captures actual coverage metrics into a coverage_report.md artifact. It never reports fabricated coverage numbers, only measured results.

Can I call Genie Code from an API to write tests?▼

No, Genie Code has no public API and cannot be invoked headlessly from an orchestrator. Use the Databricks Foundation Model API (Model Serving pay-per-token endpoints) or a Mosaic AI Agent endpoint for code generation instead.

What happens when the unit test output fails contract validation?▼

The agent performs one repair retry with the validation error appended to the context. If validation still fails, the run is routed to state.needs_human for the approval-gate agents to review.

Where are unit test artifacts stored in the Databricks pipeline?▼

Artifacts are written to /Volumes/{catalog}/state/artifacts/{ticket}/11_unit-test-agent/ in Unity Catalog Volumes. The agent also upserts tracking rows into state.artifacts and logs the full run trace to state.agent_runs.

Related Skills

Explore More Software Engineering Skills→
igot-aiigot-ai

gh-pr-review

Fetch GitHub PR changes and generate a Unity C# code-review report.

Official
Advanced
i-standard1i-standard1
6

gen-test-report

Consolidate unit, integration, and end-to-end test results into a standardized report.

Official
Intermediate
gigaverse-appgigaverse-app
4

testing

Codify Python testing best practices for unit and integration tests.

Official
Intermediate
juanlamadrid20juanlamadrid20

synthetic-data-generation

Generate realistic synthetic data for Databricks workflows using PySpark and Faker.

Community
Advanced