Audit academic manuscript claims against extraction notes with status labels.
npx skills add https://github.com/bionoob7/nlr-workflow --skill lit-audit
Please help me install this Agent Skill. Skill: lit-audit Source: https://github.com/bionoob7/nlr-workflow/tree/main/.claude/skills/lit-audit Command: npx skills add https://github.com/bionoob7/nlr-workflow --skill lit-audit
This Skill addresses the challenge of verifying the factual accuracy of academic literature review manuscripts by meticulously cross-referencing quantitative claims with source extraction notes.
Invoke the lit-audit skill to automatically audit the factual fidelity of your manuscript, providing a comprehensive report of matched, mismatched, and uncited claims.
High-intent search queries and answers about installing and using this skill.
Cross-checking quantitative claims like metric values and sample sizes against source extraction notes verifies factual accuracy in academic manuscripts. This process operates on markdown manuscript files and reports matched, mismatched, and uncited claims.
Fact-checking for academic literature reviews works by cross-referencing dataset names, model names, and metric values stated in the text against source extraction notes. It generates a comprehensive report labeling matched, mismatched, and uncited claims.
Yes, dependencies like python-bibTeX and python-zotero support auditing markdown manuscript files. These tools parse references and extraction notes to verify numerical and qualitative claims within the manuscript.
Performing a detailed factual accuracy audit is the best way to cross-check dataset names and sample sizes in research papers. This automatically verifies quantitative claims against source extraction notes and flags inconsistencies before style editing.
You need to perform a factual accuracy audit on academic manuscripts before any style editing pass. This ensures numbers, dataset names, and model metrics are consistent with their original extraction notes and sources.
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