# PDF

> PDF文件处理全能工具，支持PDF创建、合并、拆分、文本提取、表格提取、OCR识别、加密解密、添加水印、图片提取和表单填写。 Use when: "提取PDF文字", "合并PDF文件", "拆分PDF", "extract PDF text", "merge PDF files", "PDF加水印", "OCR scanned PDF", "PDF转Word". 整合pypdf、pdfplumber、reportlab、qpdf等工具，覆盖PDF全生命周期操作。Cross-references: document-hub, content-extractor, email-sender. Built by UniqueClub 🌐 https://uniqueclub.ai

- Skill: `wulaosiji/pdf` (Agent Skill, multi-file: 12 files)
- Install (CLI): `npx skillmds@latest add wulaosiji/pdf`
- Raw SKILL.md: https://api.skillmd.com/api/skills/wulaosiji/pdf/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: wulaosiji (https://skillmd.com/u/wulaosiji)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/wulaosiji/pdf

---


# PDF Processing Guide

> Comprehensive PDF processing operations using Python libraries and command-line tools.

## When to Use

Use this skill when:
- 需要提取PDF中的文本或表格数据
- 合并多个PDF文件为一个
- 将PDF拆分为单页文件
- 旋转PDF页面方向
- 为PDF添加水印
- 创建新的PDF文档
- 处理PDF表单填写
- 对PDF进行加密或解密
- OCR识别扫描版PDF
- 从PDF中提取图片

Do NOT use this skill if:
- PDF文件被密码保护且无密码 → 需获取密码后使用
- 需要复杂的PDF编辑（如修改现有内容）→ 使用专业PDF编辑器
- PDF文件损坏无法读取 → 先修复文件
- 需要保留原始PDF的复杂排版 → 提取内容可能丢失格式

Typical triggers:
- 「提取PDF文字」「合并PDF文件」「拆分PDF」
- "Extract PDF text", "Merge PDF files", "Split PDF"
- 「PDF加水印」「PDF转Word」「扫描PDF识别」
- "Add watermark to PDF", "Convert PDF to Word", "OCR scanned PDF"

## Workflow

遵循六步推进法（探查→约束→证据→执行→验证→交付）完成操作。

1. **探查 (Probe)**
完整读取用户需求，确认PDF操作类型和输入文件。检查PDF是否可读、是否加密、页数。

2. **约束 (Constrain)**
根据操作类型选择合适工具，设定边界和不可降级的交付标准。受阻时换通道，不降级交付物。

| 操作类型 | 推荐工具 | 复杂度 |
|----------|----------|--------|
| 文本提取 | pdfplumber | 简单 |
| 表格提取 | pdfplumber | 中等 |
| 合并/拆分 | pypdf / qpdf | 简单 |
| 创建PDF | reportlab | 中等 |
| OCR识别 | pytesseract | 复杂 |

3. **证据 (Evidence)**
提取的文本和表格数据必须来自PDF实际内容，不编造数据。每个数字必须可追溯到PDF原文页码。

4. **执行 (Execute)**
调用对应工具执行操作，先给影响与结论，再给行动和必要证据。
```python
from pypdf import PdfReader, PdfWriter
# 或
import pdfplumber
```

5. **验证 (Verify)**
用不同于生成路径的方式回读输出——检查输出文件完整性，验证提取的文本/数据准确性，确认格式保持正确。大型PDF分页验证。

6. **交付 (Deliver)**
返回处理结果（文件路径或提取数据），清理临时文件。

## Output

- 文本提取：返回纯文本字符串
- 表格提取：返回 `List[List[str]]` 或 pandas DataFrame
- 合并/拆分/旋转/水印：返回输出PDF文件路径
- 创建PDF：返回生成的PDF文件路径
- OCR：返回识别后的文本字符串

## Guardrails

以下约束确保安全、可靠地使用本技能。

**Anti-patterns**
- NEVER 使用Unicode上下标字符（会导致黑框），使用 `<sub>` 和 `<super>` 标签
- Do NOT 不验证提取的表格数据
- Do NOT 忽略PDF版本兼容性问题
- Do NOT 处理大型PDF时不分页处理

**Constraints**
- 扫描版PDF需要OCR才能提取文本
- 复杂排版可能丢失格式
- 某些PDF字体嵌入问题
- 加密PDF需要密码

**Important Notes**
1. **Subscripts/Superscripts**: 使用 `<sub>` 和 `<super>` 标签，不要用Unicode字符
2. **Table Extraction**: 复杂表格可能需要手动调整
3. **OCR Quality**: 依赖图片清晰度

## 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)
```

#### Extract Metadata
```python
reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
```

#### Rotate Pages
```python
reader = PdfReader("input.pdf")
writer = PdfWriter()

page = reader.pages[0]
page.rotate(90)  # Rotate 90 degrees clockwise
writer.add_page(page)

with open("rotated.pdf", "wb") as output:
    writer.write(output)
```

**pdfplumber - Text and Table Extraction**

#### Extract Text with Layout
```python
import pdfplumber

with pdfplumber.open("document.pdf") as pdf:
    for page in pdf.pages:
        text = page.extract_text()
        print(text)
```

#### Extract Tables
```python
with pdfplumber.open("document.pdf") as pdf:
    for i, page in enumerate(pdf.pages):
        tables = page.extract_tables()
        for j, table in enumerate(tables):
            print(f"Table {j+1} on page {i+1}:")
            for row in table:
                print(row)
```

#### Advanced Table Extraction
```python
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)

if all_tables:
    combined_df = pd.concat(all_tables, ignore_index=True)
    combined_df.to_excel("extracted_tables.xlsx", index=False)
```

**reportlab - Create PDFs**

#### Basic PDF Creation
```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.line(100, height - 140, 400, height - 140)

c.save()
```

#### Multi-Page PDF
```python
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
from reportlab.lib.styles import getSampleStyleSheet

doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []

story.append(Paragraph("Report Title", styles['Title']))
story.append(Spacer(1, 12))
story.append(Paragraph("Body content", styles['Normal']))
story.append(PageBreak())
story.append(Paragraph("Page 2", styles['Heading1']))

doc.build(story)
```

#### Subscripts and Superscripts
**IMPORTANT**: Never use Unicode subscript/superscript characters. Use ReportLab's XML markup:
```python
from reportlab.platypus import Paragraph

# Subscripts: use <sub> tag
chemical = Paragraph("H<sub>2</sub>O", styles['Normal'])

# Superscripts: use <super> tag  
squared = Paragraph("x<super>2</super>", styles['Normal'])
```

## Command-Line Tools

**pdftotext (poppler-utils)**
```bash
# Extract text
pdftotext input.pdf output.txt

# Preserve layout
pdftotext -layout input.pdf output.txt

# Specific pages
pdftotext -f 1 -l 5 input.pdf output.txt  # Pages 1-5
```

**qpdf**
```bash
# Merge PDFs
qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf

# Split pages
qpdf input.pdf --pages . 1-5 -- pages1-5.pdf

# Rotate pages
qpdf input.pdf output.pdf --rotate=+90:1

# Remove password
qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf
```

## Common Tasks

**OCR on Scanned PDFs**
```python
import pytesseract
from pdf2image import convert_from_path

images = convert_from_path('scanned.pdf')
text = ""
for i, image in enumerate(images):
    text += f"Page {i+1}:\n"
    text += pytesseract.image_to_string(image)
    text += "\n\n"
```

**Add Watermark**
```python
from pypdf import PdfReader, PdfWriter

watermark = PdfReader("watermark.pdf").pages[0]
reader = PdfReader("document.pdf")
writer = PdfWriter()

for page in reader.pages:
    page.merge_page(watermark)
    writer.add_page(page)

with open("watermarked.pdf", "wb") as output:
    writer.write(output)
```

**Extract Images**
```bash
pdfimages -j input.pdf output_prefix
```

**Password Protection**
```python
from pypdf import PdfReader, PdfWriter

reader = PdfReader("input.pdf")
writer = PdfWriter()

for page in reader.pages:
    writer.add_page(page)

writer.encrypt("userpassword", "ownerpassword")

with open("encrypted.pdf", "wb") as output:
    writer.write(output)
```

## 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 |
| Command line merge | qpdf | `qpdf --empty --pages ...` |
| OCR scanned PDFs | pytesseract | Convert to image first |

## Related Skills

- **document-hub** — 上级封装：Word/Excel与PDF互转的统一入口
- **content-extractor** — 内容来源：提取网络内容生成PDF
- **email-sender** — 下游分发：发送PDF附件

## About UniqueClub

Part of the UniqueClub toolkit — a collection of skills for AI-powered content creation and automation.
🌐 https://uniqueclub.ai