Tencent SkillHub · Data Analysis

Pdf Cn

PDF 文档处理 | PDF Document Processing. 读取、提取、合并、分割 PDF | Read, extract, merge, split PDFs. 支持文本提取、表格识别、注释 | Supports text extraction, table recognition, annotat...

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High Signal

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Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: LICENSE.txt, SKILL.md, _meta.json, forms.md, reference.md, scripts/check_bounding_boxes.py

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Review SKILL.md after the package is downloaded.
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I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

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I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.1

Provenance

Publisher: guohongbin-git
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 14 sections Open source page

Overview

This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see REFERENCE.md. If you need to fill out a PDF form, read FORMS.md and follow its instructions.

Quick Start

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()

pypdf - Basic Operations

Merge PDFs 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 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 reader = PdfReader("document.pdf") meta = reader.metadata print(f"Title: {meta.title}") print(f"Author: {meta.author}") print(f"Subject: {meta.subject}") print(f"Creator: {meta.creator}") Rotate Pages 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 import pdfplumber with pdfplumber.open("document.pdf") as pdf: for page in pdf.pages: text = page.extract_text() print(text) Extract Tables 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 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: # Check if table is not empty df = pd.DataFrame(table[1:], columns=table[0]) all_tables.append(df) # Combine all tables 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 from reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas c = canvas.Canvas("hello.pdf", pagesize=letter) width, height = letter # Add text c.drawString(100, height - 100, "Hello World!") c.drawString(100, height - 120, "This is a PDF created with reportlab") # Add a line c.line(100, height - 140, 400, height - 140) # Save c.save() Create PDF with Multiple Pages from reportlab.lib.pagesizes import letter from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak from reportlab.lib.styles import getSampleStyleSheet doc = SimpleDocTemplate("report.pdf", pagesize=letter) styles = getSampleStyleSheet() story = [] # Add content title = Paragraph("Report Title", styles['Title']) story.append(title) story.append(Spacer(1, 12)) body = Paragraph("This is the body of the report. " * 20, styles['Normal']) story.append(body) story.append(PageBreak()) # Page 2 story.append(Paragraph("Page 2", styles['Heading1'])) story.append(Paragraph("Content for page 2", styles['Normal'])) # Build PDF doc.build(story) Subscripts and Superscripts IMPORTANT: Never use Unicode subscript/superscript characters (₀₁₂₃₄₅₆₇₈₉, ⁰¹²³⁴⁵⁶⁷⁸⁹) in ReportLab PDFs. The built-in fonts do not include these glyphs, causing them to render as solid black boxes. Instead, use ReportLab's XML markup tags in Paragraph objects: from reportlab.platypus import Paragraph from reportlab.lib.styles import getSampleStyleSheet styles = getSampleStyleSheet() # Subscripts: use <sub> tag chemical = Paragraph("H<sub>2</sub>O", styles['Normal']) # Superscripts: use <super> tag squared = Paragraph("x<super>2</super> + y<super>2</super>", styles['Normal']) For canvas-drawn text (not Paragraph objects), manually adjust font the size and position rather than using Unicode subscripts/superscripts.

pdftotext (poppler-utils)

# Extract text pdftotext input.pdf output.txt # Extract text preserving layout pdftotext -layout input.pdf output.txt # Extract specific pages pdftotext -f 1 -l 5 input.pdf output.txt # Pages 1-5

qpdf

# Merge PDFs qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf # Split pages qpdf input.pdf --pages . 1-5 -- pages1-5.pdf qpdf input.pdf --pages . 6-10 -- pages6-10.pdf # Rotate pages qpdf input.pdf output.pdf --rotate=+90:1 # Rotate page 1 by 90 degrees # Remove password qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf

pdftk (if available)

# Merge pdftk file1.pdf file2.pdf cat output merged.pdf # Split pdftk input.pdf burst # Rotate pdftk input.pdf rotate 1east output rotated.pdf

Extract Text from Scanned PDFs

# Requires: pip install pytesseract pdf2image import pytesseract from pdf2image import convert_from_path # Convert PDF to images images = convert_from_path('scanned.pdf') # OCR each page text = "" for i, image in enumerate(images): text += f"Page {i+1}:\n" text += pytesseract.image_to_string(image) text += "\n\n" print(text)

Add Watermark

from pypdf import PdfReader, PdfWriter # Create watermark (or load existing) watermark = PdfReader("watermark.pdf").pages[0] # Apply to all pages 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

# Using pdfimages (poppler-utils) pdfimages -j input.pdf output_prefix # This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.

Password Protection

from pypdf import PdfReader, PdfWriter reader = PdfReader("input.pdf") writer = PdfWriter() for page in reader.pages: writer.add_page(page) # Add password writer.encrypt("userpassword", "ownerpassword") with open("encrypted.pdf", "wb") as output: writer.write(output)

Quick Reference

TaskBest ToolCommand/CodeMerge PDFspypdfwriter.add_page(page)Split PDFspypdfOne page per fileExtract textpdfplumberpage.extract_text()Extract tablespdfplumberpage.extract_tables()Create PDFsreportlabCanvas or PlatypusCommand line mergeqpdfqpdf --empty --pages ...OCR scanned PDFspytesseractConvert to image firstFill PDF formspdf-lib or pypdf (see FORMS.md)See FORMS.md

Next Steps

For advanced pypdfium2 usage, see REFERENCE.md For JavaScript libraries (pdf-lib), see REFERENCE.md If you need to fill out a PDF form, follow the instructions in FORMS.md For troubleshooting guides, see REFERENCE.md

Category context

Data access, storage, extraction, analysis, reporting, and insight generation.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Docs1 Scripts1 Config1 Files

SKILL.md Primary doc
forms.md Docs
reference.md Docs
scripts/check_bounding_boxes.py Scripts
_meta.json Config
LICENSE.txt Files