What problem does it solve? Manual and exploratory QA during a sprint often lacks structure: tickets get tested ad hoc, bugs are filed without consistent classification, and test artifacts drift out of sync with the TMS. This Skill drives a disciplined three-phase pipeline (Planning, Execution, Reporting) for every user story or bug, producing traceable ATP, ATS, and ATR artifacts in Jira/Xray or other trackers. ## Core Features & Use Cases - Three-phase QA pipeline: Session Start, Phase 1 Planning (triage, risk scoring, ATP authoring, test-design techniques like EP/BVA/state-transition), Phase 2 Execution (smoke test first, then UI/API/DB triforce exploration with evidence capture), and Phase 3 Reporting (ATR, QA comment, ticket transition, bug filing). - Single-ticket and batch-sprint modes: Process one story or bug, or loop through an entire sprint backlog via a SPRINT-N-TESTING framework file with resume support. - TMS modality awareness: Adapts artifact creation for jira-xray (Test/Test Set/Test Plan/Test Execution issues), jira-native, Notion, and Azure DevOps, with strict rules for coverage links and environments. - Defect classification doctrine: Distinguishes Bug vs Defect vs Improvement by feature lifecycle stage, with severity-to-priority auto-derivation. - Use Case: A QA engineer asks to "test ticket UPEX-277" — the Skill fetches the ticket, builds the PBI folder, authors an ATP with technique-derived test outlines, runs smoke plus UI/API/DB exploration capturing screenshots, then posts the ATR and QA comment and transitions the ticket. ## Quick Start Ask the AI to test a specific user story or bug ticket by its key, for example: run sprint QA on ticket PROJ-123 and report the results.
