pipeline-qa

Write and run pytest tests for pipeline code changes with a QA report.

44|24|Updated Nov 20, 2025

Search Tags:#pytest #test coverage #mocking #qa automation #test execution #pipeline validation

Ready to run

npx skills add https://github.com/redhat-community-ai-tools/UnifAI --skill pipeline-qa

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pipeline-qa
Source: https://github.com/redhat-community-ai-tools/UnifAI/tree/main/.cursor/skills/pipeline-qa
Command: npx skills add https://github.com/redhat-community-ai-tools/UnifAI --skill pipeline-qa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents shipping broken features by ensuring code changes receive comprehensive, high-quality pytest coverage and that the full test suite passes.

Core Features & Use Cases

Test coverage analysis: Identifies what needs unit, integration, edge-case, and error-path testing based on the approved design and implementation changes.
Deterministic pytest creation: Produces maintainable tests using consistent naming, fixtures, parametrization, and boundary-focused mocking.
End-to-end validation: Runs the test suite and drives a revision loop until all tests pass, along with a quality assessment of what was added.

Quick Start

Use pipeline-qa when your pipeline reaches Phase 5 (QA) to analyze the Phase 3 code changes against the Phase 2 design, write any missing pytest tests, run uv run pytest -xvs, and report a PASS/FAIL verdict with a quality assessment.

Frequently Asked Questions about pipeline-qa

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

FAQPage Schema

How do I write reliable pytest tests with confidence for new pipeline code changes?▼

To write reliable pytest tests with confidence, you need structured coverage analysis that identifies required unit, integration, edge-case, and error-path tests based on implementation changes. This ensures comprehensive, maintainable tests using proper fixtures, parametrization, and boundary-focused mocking.

What's the best way to automate QA validation for a feature implementation?▼

The best way to automate QA validation is by running the pytest test suite end-to-end and driving a revision loop until all tests pass. This process produces a QA report with pass/fail guidance and a quality assessment for the next iteration.

How does pytest test coverage analysis work for pipeline validation?▼

Pytest test coverage analysis for pipeline validation works by comparing Phase 3 code changes against the approved Phase 2 design. It identifies missing unit, integration, edge-case, and error-path tests to ensure comprehensive coverage before running validation.

Do I need to use uv run pytest to execute tests for QA automation?▼

Yes, you need to use uv run pytest -xvs to execute tests for QA automation. This specific execution command validates the test suite end-to-end and drives the revision loop until all tests pass.

Why does my pytest test suite fail to cover boundary cases and error paths?▼

Your pytest test suite fails to cover boundary cases because it lacks structured coverage analysis and boundary-focused mocking. Without comparing implementation changes against the approved design, missing edge-case and error-path tests go unidentified.

Can I use pytest fixtures and parametrization for deterministic test creation?▼

Yes, you can use pytest fixtures and parametrization for deterministic test creation. This approach ensures maintainable tests with consistent naming and boundary-focused mocking, validated by running the full test suite end-to-end.