papercutter
♥Cherished
Papercutter automates the extraction of structured data from PDF research papers and books into analysis-ready datasets. It combines PDF-to-markdown conversion with LLM-powered schema-based extraction, letting researchers configure custom data fields and generate CSV matrices or PDF reports from document collections. Built for systematic reviews and meta-analyses where manual data entry from dozens or hundreds of papers becomes infeasible.
Clauded With Love Rating
7.5 / 10
Papercutter automates extraction of structured data from PDF research papers into analysis-ready datasets using PDF-to-markdown conversion and LLM-powered schema extraction. It targets researchers conducting systematic reviews and meta-analyses who need to process dozens or hundreds of papers efficiently.
Code Quality6.5
Usefulness8.5
Claude Usage7.0
Documentation8.0
Originality7.5
Highlights
- ✓Solves a genuine pain point for researchers with a complete end-to-end pipeline from PDF ingestion to CSV output and LaTeX reports
- ✓Well-structured CLI interface with logical command progression (ingest → configure → extract → report) that matches researcher workflow
- ✓Includes concrete examples directory with real outputs from seminal ML papers and book processing, demonstrating practical value
To Improve
- →Add error handling documentation and recovery strategies for failed PDF processing or LLM extraction failures
- →Implement batch processing controls and rate limiting options for large document collections to prevent API quota exhaustion