novelty-check

To check research idea novelty, the skill systematically extracts your core technical claims and searches multiple academic databases and preprints to identify overlapping prior work. It then uses a reviewer model for cross-model verification to ensure comprehensive coverage.

A prior art search identifies existing publications related to your proposed method. This skill extracts key technical claims, performs multi-source literature searches across academic databases, and outputs a structured report pinpointing the closest existing research and key differences.

Yes, you can use it for a targeted literature review of a specific research method. It systematically searches recent literature, compares your proposed idea against prior work, and provides a structured report assessing novelty and recommending positioning for your paper.

The best way to verify an algorithm's novelty is to extract its core technical claims and search across multiple academic databases and preprints. This skill automates that process, using a reviewer model for cross-model verification to find any overlapping prior work.

Yes, the novelty check searches both academic databases and preprints. It performs multi-source literature searches to ensure recent publications are captured, then uses a reviewer model to verify the findings and identify the closest prior work.

While it systematically searches multiple databases and preprints to find prior art, novelty verification depends on the availability of recent literature in indexed sources. The cross-model reviewer helps mitigate gaps, but no automated search can guarantee absolute novelty across all unpublished work.