Citation Verification & Scholar Guide
Verifies academic references and prevents AI-generated citation hallucinations through rigorous canonical metadata cross-referencing.
This skill provides a comprehensive framework for ensuring the accuracy and integrity of academic citations within Claude Code. It directly addresses the common issue of AI-generated 'hallucinated' citations by enforcing a proactive verification workflow that checks references against canonical sources like DOI, arXiv, CrossRef, and Semantic Scholar. Designed for researchers and developers, it guides users through metadata matching, claim-level verification, and reliable BibTeX generation, ensuring that every referenced work is valid, existing, and accurately cited in scholarly publications.
Key Features
01Detection and prevention of AI-generated 'fake' or hallucinated citations
02Proactive cross-referencing against canonical scholarly databases like DOI and arXiv
03Seamless integration with ml-paper-writing workflows for academic publication
04Automated claim-level verification to ensure cited content matches source material
05Structured BibTeX generation based on verified, programmatically fetched metadata
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Use Cases
01Finding and verifying canonical identifiers like DOI and arXiv IDs for existing literature
02Validating the entire bibliography of a research paper before journal submission
03Confirming that specific technical claims are accurately supported by the cited source text