general-peer-review

What problem does it solve?

It prevents weak or under-validated research plans and manuscripts from moving forward by forcing a critical, adversarial evaluation of assumptions, baselines, sampling quality, and reproducibility.

Core Features & Use Cases

• Critical scientific review: Identifies missing baselines, flawed statistics, inadequate sampling/ensemble choices, and unjustified assumptions.
• Reproducibility and validation checks: Verifies whether protocols, hyperparameters, and reporting are sufficient for independent replication.
• Structured reviewer output: Produces a clear recommendation plus Major/Minor Concerns and Questions for authors.
• Use case: Review an AI-driven materials simulation workflow before running expensive computations by checking MD duration, supercell size, theory level, and validation against known references.

Quick Start

Ask your AI to perform peer review on the provided research plan by loading the target document and returning a structured set of Major/Minor Concerns and author questions focused on rigor and validity.