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 using the testsmells.org 19-smell academic catalog, producing a severity-ranked report with citable smell names and concrete fixes. ## Core Features & Use Cases - Polyglot Smell Detection: Analyzes tests in .NET (MSTest/xUnit/NUnit/TUnit), Python (pytest/unittest), TS/JS (Jest/Vitest/Mocha), Java (JUnit/TestNG), Go, Ruby, Rust, Swift, Kotlin, PowerShell (Pester), and C++ (GoogleTest/Catch2). - Research-Backed Taxonomy: Maps every finding to a named smell from the academic literature (Assertion Roulette, Mystery Guest, Sleepy Test, Eager Test, Sensitive Equality, and more) with calibrated severity levels. - False-Positive Calibration: Applies language-specific rules so idiomatic patterns like Go table-driven tests, pytest parametrize, and mock verifications are not misflagged. - Use Case: Point the Skill at a test project directory and receive a summary dashboard, per-smell findings with code snippets and fixes, and a prioritized remediation plan. ## Quick Start Audit the tests in my project directory using the full testsmells.org 19-smell academic catalog and give me a severity-ranked report with fixes.
