Scholar Paper Finder

The problem

Finding specific academic research on arXiv is tedious when relying on manual web searches and browser-based PDF viewing. Developers and researchers often struggle to automate literature reviews, monitor new topic trends, or generate accurate BibTeX citations for their local repositories.

What it does

• Searches arXiv using Boolean logic for keywords, authors, categories, or specific paper IDs.
• Parses Atom XML responses to extract metadata, including abstracts and submission dates.
• Downloads full-text PDFs and converts them to searchable text for deeper analysis.
• Implements a daily "watch" function to identify and diff new papers in specific research areas.
• Generates correctly formatted BibTeX entries for reference management software.

Frameworks & tools

This skill utilizes the official arXiv API, Python for XML parsing, and pdftotext for full-text extraction.

Why this beats prompting it yourself

Standard LLM prompts rely on training data that quickly becomes outdated or lack the ability to fetch the actual PDF content for verification. This skill interfaces directly with the live arXiv API and handles the specific XML parsing and BibTeX formatting rules that typically cause LLM hallucinations.

Use cases

• Automate a daily digest of new papers in specific machine learning categories.
• Retrieve and index the full text of all papers published by a specific author.
• Build a local citation library by converting arXiv IDs into clean BibTeX files.
• Perform deep keyword searches across thousands of abstracts to find niche research.

Known limitations

Cannot access paywalled literature outside of arXiv. It does not provide citation counts or impact metrics. Subject to arXiv API rate limits.