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Found 4 Skills
AI SDLC QA workflow. Use when an AI assistant is asked for QA planning, acceptance validation, regression scope, exploratory checks, smoke tests, release verification, or change-focused manual validation evidence. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
Use after QA strategy and test-case synthesis to build the requirements-to-test traceability matrix, identify missing coverage and test blockers, and score readiness for QA execution. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
Use this when you need to execute R2 in the sdlc-dev product requirement Spec process, transcribe requirements/solution.md into a deliverable, acceptable, and testable requirements/prd.md, while avoiding guessing file paths, continuing generation when solution.md is missing, or using "Pending Questions/Open Questions" to replace the verification checklist.
Use when working on the spec branch of sdlc-dev, when requirements are ambiguous, scope is unstable, constraints are unclear, and issues such as context drift, unfounded assumptions, multiple questions asked at once, or requests to skip the FEATURE_DIR/raw gate occur.