# PDF Text Extractor

> 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.

- Skill: `vamseeachanta/pdf-text-extractor` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/pdf-text-extractor`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/pdf-text-extractor/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/pdf-text-extractor

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# 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):**
```python
# 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):**
```python
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
- Preparing documents for AI/ML processing
- Building knowledge bases from PDF archives

## Related Skills

- `knowledge-base-builder` - Build searchable database from extracted text
- `semantic-search-setup` - Add vector embeddings for AI search
- `document-inventory` - Catalog documents before extraction

## Version History

- **1.3.0** (2026-03-17): WRK-1277 learnings — pdftotext preferred for batch; D-state/NFS/NTFS warnings; fixed duplicate Sub-Skills sections; updated tool selection guidance
- **1.2.0** (2026-01-04): Added OpenAI Codex workflow recommendation as preferred approach; updated Quick Start to show Codex-first workflow; added reference to `pdf` skill for markdown conversion
- **1.1.0** (2026-01-02): Added Quick Start, Execution Checklist, Error Handling, Metrics sections; updated frontmatter with version, category, related_skills
- **1.0.0** (2024-10-15): Initial release with PyMuPDF, batch processing, OCR support, metadata extraction

## Sub-Skills

- [Best Practices](best-practices/SKILL.md)
- [Execution Checklist](execution-checklist/SKILL.md)
- [Error Handling](error-handling/SKILL.md)
- [Metrics](metrics/SKILL.md)
- [Dependencies (+5)](dependencies/SKILL.md)
- [Features](features/SKILL.md)
- [Readability Classification](readability-classification/SKILL.md)
- [Encrypted PDFs (+2)](encrypted-pdfs/SKILL.md)
- [Example Usage](example-usage/SKILL.md)