stvlynn/dingtalk-wukong-skills/pdf

Claude Code and agent skill by stvlynn/dingtalk-wukong-skills

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Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.

Install

npx vskill@latest install stvlynn/dingtalk-wukong-skills/pdf

SKILL.md

PDF文档处理专家 Real Skill

一句话说明

全面的 PDF 文档处理工具,支持文本表格提取、PDF 创建、文档操作和表单填写。

PDF Processing Guide - Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms.


使用场景

适用:

  • 从 PDF 提取文本、表格数据并导出 Excel
  • 创建新的 PDF 文档(报告、证书、发票)
  • 合并、拆分、旋转 PDF 文档
  • 填写 PDF 表单(政府表单、申请表)
  • 批量处理多个 PDF 文件

不适用:

  • 扫描版 PDF(需先 OCR 处理 / requires OCR first)
  • 复杂的 PDF 编辑(用专业软件 / use professional software)

核心流程

用户需求 → 判断任务类型 → 选择工具 → 执行操作 → 验证输出
User request → Task type classification → Tool selection → Execute → Validate

任务类型判断 (Task Classification):

Extracting Data (提取数据)
  • 文本提取 (Text extraction) → pdfplumber.extract_text()
  • Better accuracy than pypdf for text extraction
  • 表格提取 (Table extraction) → pdfplumber.extract_tables() + pandas
  • Intelligent table structure recognition
  • Export to Excel, CSV, JSON
Creating PDFs (创建 PDF)
  • 简单文档 (Simple documents) → reportlab.pdfgen.canvas
  • Quick document creation with text and graphics
  • 复杂布局 (Complex layouts) → reportlab.platypus
  • Paragraphs, Tables, Images with advanced styling
PDF Operations (PDF 操作)
  • 合并 (Merge) → pypdf.PdfWriter
  • Combine multiple PDFs into one
  • 拆分 (Split) → pypdf.PdfWriter (save each page separately)
  • 旋转 (Rotate) → page.rotate()
  • 元信息 (Metadata) → pypdf.PdfReader.metadata / PdfWriter.add_metadata()
Form Processing (表单处理)
  • 查看字段 (View fields) → See forms.md
  • 填写表单 (Fill forms) → See forms.md
  • 批量处理 (Batch processing) → Loop + form filling API

任务完成标准

必须满足(缺一不可):

  • PDF 文件可正常打开
  • 提取的数据完整准确
  • 表格结构保留正确(行列对应)
  • 创建的 PDF 内容清晰可读

质量评级:

  • ⭐⭐⭐⭐⭐ 优秀 - 数据完整 + 格式规范 + 自动化批处理
  • ⭐⭐⭐ 及格 - 数据正确 + PDF 可用
  • ⭐ 失败 - 数据缺失或 PDF 损坏

参考资料(供 AI 使用)

类型路径说明
核心文档docs/00-SKILL-完整处理指南.mdComplete PDF processing guide (~300 lines)
表单处理docs/forms.mdPDF form filling detailed tutorial
高级参考docs/reference.mdJavaScript libraries and advanced features

关键原则(AI 必读 / Critical Principles)

1. Prefer pdfplumber for Extraction (优先使用 pdfplumber)
  • 文本提取: More accurate than pypdf
  • 表格识别: Strong table recognition capability
  • 布局保留: Preserves layout information
  • 复杂 PDF: Better for complex PDF processing
2. Table Data Normalization (表格数据规范化)
python
# Best practice for table extraction
import pdfplumber
import pandas as pd
all_tables = []
with pdfplumber.open("document.pdf") as pdf:
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
if table: # Check not empty
df = pd.DataFrame(table[1:], columns=table[0]) # First row as header
all_tables.append(df)
# Combine and clean
combined = pd.concat(all_tables, ignore_index=True)
combined.to_excel("output.xlsx", index=False)
3. Scanned PDFs Require OCR (扫描版需要 OCR)
  • 识别: If text extraction returns empty or garbled text
  • 处理: Must use OCR tools first (Tesseract, Adobe Acrobat, etc.)
  • 告知用户: Clearly inform user that OCR pre-processing is needed
4. Memory Management for Large Files (大文件内存管理)
  • Batch processing for multiple PDFs
  • Process pages sequentially rather than loading entire document
  • Use generators when possible

快速命令参考 (Quick Commands)

Text Extraction (文本提取)
python
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
print(text)
Table to Excel (表格转 Excel)
python
import pdfplumber
import pandas as pd
all_tables = []
with pdfplumber.open("document.pdf") as pdf:
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)
combined = pd.concat(all_tables, ignore_index=True)
combined.to_excel("extracted_tables.xlsx", index=False)
Merge PDFs (合并 PDF)
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 (拆分 PDF)
python
from pypdf import PdfReader, PdfWriter
reader = PdfReader("large_document.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)
Create PDF (创建 PDF)
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()
Extract Metadata (提取元信息)
python
from pypdf import PdfReader
reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
print(f"Pages: {len(reader.pages)}")

依赖安装 (Dependencies Installation)

Python Dependencies
bash
pip install -r requirements.txt
# Includes: pypdf, pdfplumber, reportlab, pandas, openpyxl

常见场景示例 (Common Scenarios)

Scenario 1: Batch Extract Invoice Tables (批量提取发票表格)
python
import pdfplumber
import pandas as pd
import glob
all_data = []
for pdf_file in glob.glob("invoices/*.pdf"):
with pdfplumber.open(pdf_file) as pdf:
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
if table:
df = pd.DataFrame(table[1:], columns=table[0])
df['source_file'] = pdf_file
all_data.append(df)
combined = pd.concat(all_data, ignore_index=True)
combined.to_excel("all_invoices.xlsx", index=False)
Scenario 2: Create Report with Table (生成带表格的报告)
python
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph
from reportlab.lib.styles import getSampleStyleSheet
doc = SimpleDocTemplate("report.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []
# Title
story.append(Paragraph("Sales Report", styles['Title']))
# Table
data = [
['Product', 'Q1', 'Q2', 'Q3', 'Q4'],
['Product A', '1000', '1200', '1100', '1300'],
['Product B', '800', '900', '950', '1000']
]
t = Table(data)
t.setStyle(TableStyle([
('BACKGROUND', (0, 0), (-1, 0), 'grey'),
('TEXTCOLOR', (0, 0), (-1, 0), 'white'),
('ALIGN', (0, 0), (-1, -1), 'CENTER'),
('GRID', (0, 0), (-1, -1), 1, 'black')
]))
story.append(t)
doc.build(story)

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