qa-tester

Automating QA test execution involves selecting pending tests, running CLI and MCP protocol calls in order, and validating outputs against expected results. This Skill automates that entire workflow, including configuration profile switching and result logging.

Automated QA testing handles failures by diagnosing errors, applying minimal fixes, and retrying up to three rounds. It preserves a clear audit trail in QA_TEST_PROGRESS.md to ensure Streamlit dashboard checks pass based on concrete terminal evidence.

Yes, YAML configuration editing is required for end-to-end data lifecycle tests. The testing process depends on deterministic subprocess control and YAML configuration profiles to switch contexts and validate multi-step data lifecycle scenarios accurately.

The best way to verify QA test results is through strict verification discipline requiring concrete terminal evidence. This approach enforces section-end validation to catch weak or inferred notes, ensuring pass criteria are evidence-based rather than manually inspected.

Yes, you can use this QA testing approach for multi-step data lifecycle scenarios in a Modular RAG MCP Server. It bootstraps state, executes relevant tests across CLI and dashboard interfaces, verifies results, and updates the progress log automatically.

QA test automation requires progress note validation to catch weak or inferred notes during execution. It enforces section-end validation with retries, ensuring that progress logs in QA_TEST_PROGRESS.md contain verified, evidence-based pass results rather than assumptions.