Comprehensive PDF manipulation toolkit for extracting text and tables,
Best use case
pdf is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Comprehensive PDF manipulation toolkit for extracting text and tables,
Teams using pdf should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/pdf/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How pdf Compares
| Feature / Agent | Standard Approach | |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Comprehensive PDF manipulation toolkit for extracting text and tables,
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# PDF Processing Guide
## Quick Start
```python
from pypdf import PdfReader, PdfWriter
# Read a PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")
# Extract text
text = ""
for page in reader.pages:
text += page.extract_text()
```
## Python Libraries
### pypdf - Basic Operations
#### Merge PDFs
```python
from pypdf import PdfWriter, PdfReader
writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
reader = PdfReader(pdf_file)
for page in reader.pages:
writer.add_page(page)
with open("merged.pdf", "wb") as output:
writer.write(output)
```
#### Split PDF
```python
reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
writer = PdfWriter()
writer.add_page(page)
with open(f"page_{i+1}.pdf", "wb") as output:
writer.write(output)
```
### pdfplumber - Text and Table Extraction
#### Extract Tables
```python
import pdfplumber
import pandas as pd
with pdfplumber.open("document.pdf") as pdf:
all_tables = []
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
if table:
df = pd.DataFrame(table[1:], columns=table[0])
all_tables.append(df)
```
### reportlab - Create PDFs
```python
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas("hello.pdf", pagesize=letter)
width, height = letter
c.drawString(100, height - 100, "Hello World!")
c.save()
```
## Command-Line Tools
```bash
# Extract text (poppler-utils)
pdftotext input.pdf output.txt
# Merge PDFs (qpdf)
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf
# Split pages
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
```
## Quick Reference
| Task | Best Tool | Command/Code |
|------|-----------|--------------|
| Merge PDFs | pypdf | `writer.add_page(page)` |
| Split PDFs | pypdf | One page per file |
| Extract text | pdfplumber | `page.extract_text()` |
| Extract tables | pdfplumber | `page.extract_tables()` |
| Create PDFs | reportlab | Canvas or Platypus |
| OCR scanned PDFs | pytesseract | Convert to image first |
## Scientific Skill Interleaving
This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:
### Graph Theory
- **networkx** [○] via bicomodule
- Universal graph hub
### Bibliography References
- `general`: 734 citations in bib.duckdb
## Cat# Integration
This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:
```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```
### GF(3) Naturality
The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```
This ensures compositional coherence in the Cat# equipment structure.Related Skills
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