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Evaluate AI-generated code across many dimensions as an independent quality gate.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Judges Panel" yet — see the docs or source repo.
Evaluate this AI-generated code across multiple quality dimensions and highlight key risks and improvements.
A dimension-by-dimension evaluation with code quality risks and improvement suggestions.
Assess this candidate code as a pre-merge check and decide whether it passes the quality gate, with reasons.
A pass/fail gate decision with the evaluation evidence behind it.
Perform a deep review of this complex AI-generated code, going beyond basic pattern checks for richer quality judgment.
A combined assessment that includes baseline checks and deeper review findings.
Developers or teams can independently evaluate AI-generated code before merging it into the main branch. It works well as a quality gate to surface issues early.
Researchers or engineering teams can use it to review AI-generated code across specialized dimensions instead of relying on a single metric. It is useful for comparing code quality across models or prompting strategies.
It evaluates the quality of AI-generated code across 45 specialized dimensions. It can also serve as an independent quality gate.
The description says it uses deterministic pattern matching and optional LLM-powered deep review. For the exact evaluation flow, see the source repository.
Not necessarily. The description says deep review is optional, so at least part of the evaluation can be done through deterministic pattern matching; see the source repository for exact prerequisites.
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