What problem does it solve? It turns scattered product requirements, prototypes, interfaces, code, and test records into a single auditable QA evidence chain, so test plans, execution results, bugs, and release judgments stay consistent and traceable instead of being assembled ad hoc. ## Core Features & Use Cases - Controlled QA Workflow: A stage controller (bootstrap, anchor, complete) manages baseline, design, execution, change, and release phases with gate checks that block incorrect counts, verdicts, or P0 classifications. - Multi-Platform Testing Support: Covers Web, API, iOS, Android, and WeChat mini-program testing with environment detection scripts, device matrix runners, OpenAPI-driven test manifest generation, and manual handoff packages when automation is blocked. - Evidence-Based Bug and Release Discipline: Enforces S1-S4 severity and P0-P3 priority rules, preserves first-failure evidence, and only allows go/no-go verdicts backed by formal execution evidence. - Use Case: Given a PRD and a web app URL, the skill builds a requirement-risk-case traceability model, executes P0 journeys, files bugs with reproduction evidence, and delivers a release decision report as a document or spreadsheet. ## Quick Start Ask the AI to create a test plan and execute QA for your feature by providing the PRD or project directory and the target URL or build.