Extract metadata, findings, and comparison vectors from academic papers.
npx skills add https://github.com/gy-hou/trendr --skill paper-analyzer-gy-hou
Please help me install this Agent Skill. Skill: paper-analyzer Source: https://github.com/gy-hou/trendr/tree/main/skills/paper-analyzer Command: npx skills add https://github.com/gy-hou/trendr --skill paper-analyzer-gy-hou
This tool extracts essential information from academic papers to create consistent, reusable notes and enable side-by-side comparisons.
Simply provide a paper identifier or file path and instruct the tool to extract structured notes.
High-intent search queries and answers about installing and using this skill.
To extract structured notes for a literature review, you provide a paper identifier or file path to generate standardized metadata, key findings, and comparison vectors from academic papers. This automates the creation of consistent, reusable notes across disciplines.
Yes, you can use arXiv abstracts and Semantic Scholar APIs to build a literature comparison matrix. The tool supports major API sources and local PDFs to extract comparison vectors and generate a standardized matrix for downstream analysis.
You can automate literature reviews across disciplines by extracting structured metadata and comparison vectors from various paper sources. Providing local PDFs or API identifiers allows the tool to generate consistent notes and a literature matrix regardless of the academic field.
Yes, the paper analyzer works with local PDFs and OpenAlex for data retrieval. It supports local file paths alongside API sources like OpenAlex and Semantic Scholar to extract essential information and generate standardized literature notes.
The best way to generate standardized notes from academic papers is using a structured extraction tool that formats metadata, highlights, and references into a consistent template. This ensures notes are reusable and formatted for side-by-side comparisons.
When extracting structured metadata, local PDFs require direct file path access while API sources like Semantic Scholar require network connectivity. Both methods yield standardized notes, but API sources may provide more consistent metadata formatting across disciplines.
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