pdf-text-extractor

name	pdf-text-extractor
description	Extract text from PDF files with intelligent chunking and metadata preservation. For batch extraction (1K+ PDFs), use pdftotext (poppler) via subprocess — see pdf skill Tool Selection table. For single-doc quality, use Codex or PyMuPDF. Supports technical documents, standards libraries, research papers, or any PDF collection.
type	reference
version	1.2.0
last_updated	"2026-01-04T00:00:00.000Z"
category	data
related_skills	["pdf","knowledge-base-builder","semantic-search-setup","document-inventory"]
capabilities	[]
requires	[]
see_also	["pdf-text-extractor-readability-classification"]
tags	[]

Pdf Text Extractor

Overview

This skill extracts text from PDF files using PyMuPDF (fitz), with intelligent chunking, page tracking, and metadata preservation. Handles large PDF collections with batch processing and error recovery.

Tool selection (see pdf skill Tool Selection table for full guidance):

• Batch (1K+ PDFs): pdftotext (poppler) via subprocess.run(timeout=30) — 37x faster, reliable timeouts
• Single doc quality: OpenAI Codex PDF→Markdown (best understanding)
• Single doc text: PyMuPDF (fitz) — fast, good API

WARNING (WRK-1277): Do NOT use pdfplumber in multiprocessing pools. It hangs in kernel D-state on NTFS/NFS mounts — uninterruptible by SIGALRM. Use pdftotext via subprocess for all parallel/batch work.

Note: The doc-intelligence pipeline uses pdfplumber for single-document extraction. For bulk extraction across the 1M+ corpus, use pdftotext via subprocess (see pdf/pdftotext-poppler sub-skill for the proven batch pattern).

Quick Start

Recommended Approach (with Codex conversion):

# 1. Convert PDF to markdown first (see pdf skill)
from pdf_skill import pdf_to_markdown_codex

md_path = pdf_to_markdown_codex("document.pdf")

# 2. Process the markdown
with open(md_path) as f:
    markdown = f.read()
    # Work with structured markdown

Direct Extraction (when Codex not needed):

import fitz  # PyMuPDF

doc = fitz.open("document.pdf")
for page in doc:
    text = page.get_text()
    print(text)
doc.close()

When to Use

• Processing PDF document collections for search indexing
• Extracting text from technical standards and specifications
• Converting PDF libraries to searchable text databases