Beyond Static Checklists: A Dynamic, Modular Toolkit for Automated Metadata Assessment

As the volume of research output accelerates, the rigorous validation of scientific publications, and the assessment of their claims, objectives, and reproducibility, have become critical bottlenecks. Traditional manual verification is labor-intensive and unscalable, often failing to keep pace with the growing need for structured assessment. To address this, we introduce a domain-agnostic, web-based toolkit designed to automate the assessment of scientific publications and transform the quality control workflow from a static box-ticking exercise into a dynamic metadata verification process. Moving beyond fixed checklist structures, this toolkit allows users to design custom checklists and review workflows through a visual, no-code designer. This enables domain experts to define specific metadata requirements (e.g., FAIR principles, reproducibility standards, or novel contribution tagging) without programming expertise. For advanced use cases, the toolkit offers a modular architecture that supports custom Python-based extensions, allowing research labs to integrate their own external analysis tools (e.g., for code repository or dataset analysis) directly into the review process definition. The system leverages these workflows alongside flagship Large Language Models (LLMs), such as GPT, Gemini, and open-weight alternatives, to automatically extract and validate information against user-defined schemas. We validate this toolkit using checklists in the field of Machine Learning as a primary case study for extracting provenance metadata. Furthermore, we illustrate how the tool facilitates collaborative verification by allowing users to assess AI outputs via an integrated human review desk to ensure reliability.