novelty-check

To check research novelty, this Skill verifies your proposed method by extracting claims and searching recent literature across arXiv, Semantic Scholar, and major conferences to identify overlaps and gaps.

Yes, you can perform a literature review targeting AI, ML, and robotics domains, orchestrating searches across arXiv, Google Scholar, and Semantic Scholar specifically for papers published between 2024 and 2026.

You extract paper metadata and abstracts by providing your method description to initiate the Skill, which then orchestrates multi-source web searches to summarize related work and identify known baselines.

Yes, novelty verification uses cross-model checking by routing extracted literature data through a Codex MCP reviewer to validate findings and generate a structured report for publication decisions.

The best way to verify a proposed method's novelty is to run a systematic comparison against recent literature and known baselines, culminating in cross-model verification and structured reporting.

Limitations of automated novelty assessment include dependency on multi-source web search coverage and the accuracy of abstract extraction, meaning it guides publication decisions rather than guaranteeing absolute novelty.