QA & Acceptance Test Scenarios

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Generates comprehensive QA test matrices and acceptance scenarios from product specs, covering happy paths, edge cases, tenant isolation, and AI evals.

The Test Scenarios skill bridges the gap between product specifications and quality assurance by transforming user stories into rigorous, prioritized test matrices. Grounded in agile testing methodologies, it methodically maps out happy paths, edge boundaries, error handling, state concurrency, and multi-tenant authorization barriers. Unlike traditional test generators, it also constructs dedicated evaluation suites for probabilistic ML and generative AI features using golden datasets and aggregate quality bars, ensuring your team catches critical security vulnerabilities, regression bugs, and data anomalies before production sign-off.

Características Principales

01State-transition and concurrency analysis for race conditions, double-submits, and stale reads
02Specialized ML and AI quality-bar evals using golden datasets, aggregate metrics, and safety checks
03Comprehensive test matrix generation covering happy paths, boundary conditions, and error recovery
04P0 tenant and workspace isolation testing to prevent cross-tenant data leaks and authorization bypasses
05Real-world data variation tests handling unexpected encodings, timezones, and malformed inputs
066 GitHub stars

Casos de Uso

01Preparing QA acceptance criteria and sign-off checklists before declaring a feature release-ready
02Stress-testing multi-tenant access boundaries, role changes, and data permissions across APIs and URLs
03Defining automated golden-set eval suites and quality thresholds for generative AI and search features