pdf-extractor
Extract text, tables, and images from PDF files. Use when converting PDF documentation, manuals, or reports to searchable text.
When & Why to Use This Skill
The PDF Extractor skill is a high-performance tool designed to transform static PDF documents into structured, searchable, and actionable data. It provides comprehensive extraction capabilities for text, complex tables, and embedded images, supporting diverse PDF types including text-based, scanned (via integrated OCR), and password-protected files. By automating the conversion of technical manuals, financial reports, and legal documentation into organized formats, it significantly enhances data accessibility and streamlines knowledge management workflows for AI agents.
Use Cases
- Digitizing Legacy Archives: Convert scanned paper records and historical manuals into searchable digital text using advanced OCR processing to unlock trapped information.
- Financial & Data Analysis: Automatically extract structured tables from complex financial statements, annual reports, or audit documents for immediate use in spreadsheets or databases.
- Knowledge Base Migration: Streamline the process of moving technical documentation and product manuals into modern, searchable knowledge bases by preserving document structure and assets.
- Automated Information Retrieval: Efficiently pull specific data points, images, and text sections from large-scale PDF repositories to support research, compliance, and decision-making.
| name | pdf-extractor |
|---|---|
| description | Extract text, tables, and images from PDF files. Use when converting PDF documentation, manuals, or reports to searchable text. |
PDF Extractor Skill
Purpose
Single responsibility: Extract structured content (text, tables, images) from PDF files into organized, searchable formats. (BP-4)
Grounding Checkpoint (Archetype 1 Mitigation)
Before executing, VERIFY:
- PDF file exists and is readable (
file <path>confirms PDF format) - PDF is not corrupted (
pdfinfo <path>returns metadata) - Password known if encrypted
- Output directory is writable
- Required tools available (pdfplumber, pytesseract for OCR)
DO NOT proceed without verification. Inspect PDF metadata first.
Uncertainty Escalation (Archetype 2 Mitigation)
ASK USER instead of guessing when:
- PDF appears to be scanned (needs OCR) but OCR tools unavailable
- Multiple table formats detected - unclear which parser to use
- Password-protected but no password provided
- Image extraction quality unclear (resolution, format preferences)
- Language detection needed for OCR
NEVER assume PDF structure without inspection.
Context Scope (Archetype 3 Mitigation)
| Context Type | Included | Excluded |
|---|---|---|
| RELEVANT | Target PDF, extraction options, output path | Other PDF files |
| PERIPHERAL | Similar PDF structure examples | Unrelated documents |
| DISTRACTOR | Previous extraction attempts | Other file formats |
Workflow Steps
Step 1: Inspect PDF (Grounding)
# Check file type
file document.pdf
# Get PDF metadata
pdfinfo document.pdf
# Check page count
pdfinfo document.pdf | grep Pages
# Check if encrypted
pdfinfo document.pdf | grep Encrypted
Step 2: Determine Extraction Strategy
| PDF Type | Detection | Strategy |
|---|---|---|
| Text-based | pdftotext produces readable text |
Direct extraction |
| Scanned/Image | pdftotext produces empty/garbled |
OCR required |
| Mixed | Some pages text, some images | Hybrid approach |
| Tables | Visual grid patterns | Table extraction mode |
| Forms | Interactive fields | Form field extraction |
Step 3: Execute Extraction
Option A: With skill-seekers (if installed)
# Basic extraction
skill-seekers pdf --pdf document.pdf --name myskill
# With table extraction
skill-seekers pdf --pdf document.pdf --name myskill --extract-tables
# With OCR for scanned docs
skill-seekers pdf --pdf document.pdf --name myskill --ocr
# With parallel processing (large PDFs)
skill-seekers pdf --pdf document.pdf --name myskill --parallel --workers 8
# Password-protected
skill-seekers pdf --pdf document.pdf --name myskill --password "secret"
Option B: Manual extraction guidance
# Basic text extraction
pdftotext -layout document.pdf output.txt
# Extract with page markers
pdftotext -layout -eol unix document.pdf output.txt
# Extract images
pdfimages -all document.pdf images/
# OCR scanned PDF (requires tesseract)
pdftoppm document.pdf page -png
tesseract page-*.png output -l eng
Step 4: Validate Output
# Check extraction quality
head -100 output/<skill-name>/references/content.md
# Verify table extraction
grep -A 10 "| " output/<skill-name>/references/*.md
# Check image extraction
ls -la output/<skill-name>/assets/images/
Recovery Protocol (Archetype 4 Mitigation)
On error:
- PAUSE - Stop extraction, preserve partial output
- DIAGNOSE - Check error type:
File not found→ Verify pathPassword required→ Ask user for passwordCorrupt PDF→ Try repair withqpdf --checkOCR failed→ Check tesseract installation, language packsMemory error→ Process in chunks, reduce workers
- ADAPT - Switch strategy based on diagnosis
- RETRY - Resume with adapted approach (max 3 attempts)
- ESCALATE - Ask user for guidance
Checkpoint Support
State saved to: .aiwg/working/checkpoints/pdf-extractor/
For large PDFs, extraction saves progress per chunk:
checkpoints/pdf-extractor/
├── document_metadata.json
├── pages_1-50.json
├── pages_51-100.json
└── current_position.json
Output Structure
output/<skill-name>/
├── SKILL.md # Skill description with PDF summary
├── references/
│ ├── index.md # Table of contents
│ ├── chapter_1.md # Content by section
│ ├── chapter_2.md
│ └── tables.md # Extracted tables
└── assets/
└── images/ # Extracted images (if enabled)
├── page_1_fig_1.png
└── page_5_chart_1.png
Configuration Options
{
"name": "mymanual",
"description": "Product manual documentation",
"pdf_path": "docs/manual.pdf",
"extract_options": {
"chunk_size": 10,
"min_quality": 6.0,
"extract_images": true,
"min_image_size": 150,
"ocr_enabled": false,
"ocr_language": "eng",
"table_extraction": true
},
"categories": {
"getting_started": ["introduction", "setup", "installation"],
"usage": ["using", "operation", "guide"],
"reference": ["appendix", "specifications", "api"]
}
}
Extraction Quality Metrics
| Metric | Good | Acceptable | Poor |
|---|---|---|---|
| Text extraction rate | >95% | 80-95% | <80% |
| Table accuracy | >90% | 70-90% | <70% |
| Image quality | >300 DPI | 150-300 DPI | <150 DPI |
| OCR confidence | >90% | 70-90% | <70% |
Troubleshooting
| Issue | Diagnosis | Solution |
|---|---|---|
| Garbled text | Scanned PDF | Enable OCR mode |
| Missing tables | Complex layout | Use --extract-tables with pdfplumber |
| Poor OCR | Low resolution | Increase DPI, check language pack |
| Memory error | Large PDF | Use chunked extraction, reduce workers |
| Corrupt PDF | File damaged | Try qpdf --check or mutool clean |
Dependencies
Required:
- Python 3.10+
- pdfplumber or pypdf
Optional (for advanced features):
- pytesseract + tesseract-ocr (for OCR)
- Pillow (for image processing)
- camelot-py (for complex tables)
References
- Skill Seekers PDF Support: https://github.com/jmagly/Skill_Seekers/blob/main/docs/PDF_MCP_TOOL.md
- REF-001: Production-Grade Agentic Workflows (BP-1, BP-4)
- REF-002: LLM Failure Modes (Archetype 1-4 mitigations)