Detects APIs, imports, methods, and patterns that are commonly hallucinated by AI code generators — non-existent standard library functions, fabricated package names, phantom methods, and incorrect API signatures that look plausible but don't exist.
Detects mismatches between stated intent (comments, docstrings, function names) and actual implementation — stubs, TODO-only bodies, misleading names, and empty implementations that AI code generators commonly produce.
Detects logic errors common in AI-generated code: inverted conditions, off-by-one errors, dead code branches, function name/implementation mismatches, and incomplete control flow.
Detects stylistic fingerprints characteristic of specific AI code generators (ChatGPT/GPT-4, Claude, Copilot, Gemini) to flag code that may carry model-specific biases, hallucinations, or blind spots.
Detects self-contradicting patterns: duplicate function definitions, contradictory boolean assignments, dead code after returns, conflicting configs, and TODO density.
Detects unnecessary abstractions, premature generalisation, wrapper-mania, and design-pattern misuse. Especially relevant for AI-generated code which tends toward over-abstraction.
Evaluates code for OS/platform independence, vendor lock-in avoidance, cross-environment compatibility, and abstraction of platform-specific functionality.
Evaluates code for error recovery, retry logic, circuit breakers, graceful degradation, idempotency, dead letter queues, chaos readiness, and fault tolerance.
Evaluates code for its ability to handle growth — horizontal/vertical scaling readiness, statelessness, concurrency, bottlenecks, and performance under load.
Holistic security assessment covering insecure data flows, weak cryptography, missing security controls, unsafe deserialization, XML external entities, prototype pollution, and other broad vulnerability patterns across all supported languages.
Evaluates code for test-to-code ratio, test isolation, mocking strategy, edge case coverage, flaky test patterns, and test pyramid balance (unit/integration/e2e).
Evaluates code for user experience patterns including loading states, error feedback, responsive design, mobile-friendliness, and interaction quality.
★not rated 7 2mo agoA29 tokens
originalMIT
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