Run2 pdf extraction

Skill cxcscmu/SkillLearnBench/skills/b2-self-feedback-claude-haiku-4-5/organize-messy-files/run2_pdf-extraction

[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.

Install

npx -y skills add cxcscmu/SkillLearnBench --skill run2_pdf-extraction

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What its author says it does

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Robust PDF text extraction with fallback strategies and error handling for academic papers and documents

SKILL.md

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PDF Text Extraction Skill (Improved)

Overview

Extract text from PDF files with improved robustness, handling corrupt PDFs gracefully. Multiple extraction strategies ensure maximum content recovery.

Installation

pip install pdfplumber pypdf pymupdf -q

Usage Examples

Primary Strategy: pdfplumber (Most Reliable)

import pdfplumber
import PyPDF2

def extract_pdf_content_robust(pdf_path, max_pages=5):
    """
    Extract text from PDF with fallback strategies.
    Tries pdfplumber first, then falls back to PyPDF2.
    """
    text = ""

    # Strategy 1: pdfplumber (best for modern PDFs)
    try:
        with pdfplumber.open(pdf_path) as pdf:
            for i, page in enumerate(pdf.pages[:max_pages]):
                extracted = page.extract_text()
                if extracted:
                    text += extracted + " "
        if len(text.strip()) > 100:  # If we got substantial text
            return text
    except Exception as e:
        pass  # Try fallback

    # Strategy 2: PyPDF2 (fallback for problematic PDFs)
    try:
        with open(pdf_path, 'rb') as f:
            reader = PyPDF2.PdfReader(f)
            for page in reader.pages[:max_pages]:
                text += page.extract_text() + " "
        if len(text.strip()) > 100:
            return text
    except Exception as e:
        pass

    return text or ""

Optimized Extraction for Academic Papers

def extract_paper_metadata(pdf_path, max_pages=3):
    """
    Extract key content from academic papers:
    - Title (usually on first page)
    - Abstract (usually first 3 pages)
    - Keywords (sometimes in abstract area)
    """
    try:
        with pdfplumber.open(pdf_path) as pdf:
            # Extract first 3 pages which contain title and abstract
            text = ""
            for page in pdf.pages[:max_pages]:
                text += page.extract_text() or ""
            return text
    except:
        return ""

Key Improvements

Dual extraction strategy: pdfplumber + PyPDF2 fallback
Robustness: Handles corrupt/damaged PDFs gracefully
Academic focus: Optimized for extracting paper abstracts and titles
Efficiency: Returns early if sufficient text extracted
Error isolation: Failures in one method don't prevent trying alternatives

Best Practices

For academic papers, first 3-5 pages contain most essential information
Title and abstract are usually in first 2 pages
Some PDFs are scanned images - these need OCR (pymupdf with OCR)
Check text length to verify successful extraction
Handle encoding issues gracefully