paper-read-pdf

name	paper-read-pdf
description	Read and extract text from a local research paper PDF file. Use when the user has a PDF file on disk (e.g. downloaded from a conference or journal) and wants to read its content, extract specific sections (abstract, methodology, results, conclusion), or understand the paper's structure. Requires the [pdf] optional dependency group.

Paper Read PDF — Local PDF Deep Reader

Extract and structure the full text of a research paper from a local PDF file. Detects standard academic sections automatically.

Usage

python ${CLAUDE_SKILL_DIR}/scripts/read_pdf_paper.py "<path/to/paper.pdf>" [--sections SECTION ...] [--pages N-M]

Arguments

Argument	Required	Description
<pdf_path>	✅	Absolute or relative path to the PDF file
--sections	❌	One or more section names to extract (see below). Default: all
--pages N-M	❌	Page range, e.g. 1-5 or 3 (1-indexed). Default: all pages

Section names (--sections filter)

Name	Matches headings like…
abstract	Abstract
introduction	Introduction, Motivation, Overview
related	Related Work, Background, Prior Work, Literature Review
method	Method, Methodology, Approach, Model, Architecture, Framework, Proposed
experiments	Experiments, Experimental Setup, Evaluation, Benchmark
results	Results, Analysis, Discussion, Findings
conclusion	Conclusion, Summary, Future Work, Limitations
references	References, Bibliography

Examples

# Extract the full paper
python ${CLAUDE_SKILL_DIR}/scripts/read_pdf_paper.py "/downloads/attention_is_all_you_need.pdf"

# Extract only the methodology and results
python ${CLAUDE_SKILL_DIR}/scripts/read_pdf_paper.py "paper.pdf" --sections method results

# Read only the first 5 pages
python ${CLAUDE_SKILL_DIR}/scripts/read_pdf_paper.py "paper.pdf" --pages 1-5

# Get just the abstract and conclusion
python ${CLAUDE_SKILL_DIR}/scripts/read_pdf_paper.py "paper.pdf" --sections abstract conclusion

Output

Returns structured markdown with:

• File info — filename, total pages, library used
• Detected sections — each section as a labeled block
• If no sections detected, returns raw text per page

Requirements

Install the [pdf] optional group:

uv pip install -e ".[pdf]"
# installs: pymupdf (primary), pypdf (fallback)

The script tries pymupdf (fitz) first — better for multi-column layouts common in conference papers (NeurIPS, ICML, ACL, CVPR). Falls back to pypdf if not installed.

Notes

• Works with any research PDF — arXiv downloads, conference papers, journal articles
• For arXiv papers where you only have the ID (not a local file), use paper-fetch instead
• Very large PDFs (100+ pages) are capped at 80 000 characters
• Scanned PDFs (image-only) cannot be extracted — text must be embedded