What problem does it solve? Test suites accumulate design flaws like conditional logic, external resource dependencies, and missing assertions that make tests fragile, slow, and misleading. This Skill performs a formal audit of test code against the academic test smell taxonomy, producing a severity-ranked report with citable smell names and concrete fixes. ## Core Features & Use Cases - Academic Smell Detection: Identifies 10 core smells (Conditional Test Logic, Mystery Guest, Sleepy Test, Assertion-Free Test, Eager Test, Magic Number Test, Sensitive Equality, Exception Handling, General Fixture, Ignored Test) plus 9 extended smells from the research literature. - Polyglot Framework Support: Covers .NET (MSTest/xUnit/NUnit/TUnit), Python (pytest/unittest), JS/TS (Jest/Vitest/Mocha), Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell Pester, and C++ via language extension files. - Calibrated Reporting: Applies calibration rules to avoid false positives on integration tests, table-driven tests, parametrized tests, and idiomatic patterns, then outputs a severity dashboard with prioritized remediation. - Use Case: Point the Skill at a flaky test project and receive a report naming each smell (e.g., Mystery Guest in OrderServiceTests.Checkout_Works), its severity, a code snippet, and a concrete fix. ## Quick Start Audit the test files in my project for test smells using the academic testsmells.org catalog and give me a severity-ranked report with fixes.
