What problem does it solve?
It prevents test suites from becoming ineffective by ensuring each test validates meaningful behavior instead of repeating implementation details or asserting tautologies.
Core Features & Use Cases
- Meaningful assertions: Enforces behavior-focused expectations that would fail if the product regresses.
- Test design guardrails: Requires naming that captures behavior and condition, and enforces one action under test per
it() block.
- Anti-pattern detection for reviews: Rejects shadow reimplementation, over-mocking, tautological assertions, and implementation-state testing.
- Supabase-specific testing guidance: Ensures Supabase client creation is mocked so tests don’t hit real services, while keeping domain data realistic (Spanish fields like
nombre, costo, prioridad, estado).
Quick Start
Ask the AI to review the changes in your pull request and identify any tests that violate the meaningful-tests rules, including naming, one-act-per-test, mocking strategy, and anti-patterns like shadow reimplementation.