Emanuel Almeida
6b3a6f2698
- All SKILL.md files now <500 lines (avg reduction 69%) - Detailed content extracted to references/ subdirectories - Frontmatter standardised: only name + description (Anthropic standard) - New skills: brand-guidelines, spec-coauthor, report-templates, skill-creator - Design skills: anti-slop guidelines, premium-proposals reference - Removed non-standard frontmatter fields (triggers, version, author, category) Plugins affected: infraestrutura, marketing, dev-tools, crm-ops, gestao, core-tools, negocio, perfex-dev, wordpress, design-media Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
797 lines
21 KiB
Markdown
797 lines
21 KiB
Markdown
---
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name: pdf
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description: Processamento completo de ficheiros PDF — leitura, extraccao de texto/tabelas, merge, split, watermarks, encriptacao, OCR, criacao e preenchimento de formularios.
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---
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# PDF Processing Guide
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## Resumo
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Guia completo para processamento de PDFs com bibliotecas Python e ferramentas de linha de comandos. Para formularios PDF, seguir as instruccoes na seccao "Preenchimento de formularios". Para funcionalidades avancadas e bibliotecas JavaScript, consultar a seccao "Referencia avancada".
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## Quick Start
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```python
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from pypdf import PdfReader, PdfWriter
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# Read a PDF
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reader = PdfReader("/media/ealmeida/Dados/GDrive/Cloud/Descomplicar/documento.pdf")
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print(f"Pages: {len(reader.pages)}")
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# Extract text
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text = ""
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for page in reader.pages:
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text += page.extract_text()
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```
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## Bibliotecas Python
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### pypdf — operacoes basicas
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#### Merge PDFs
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```python
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from pypdf import PdfWriter, PdfReader
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writer = PdfWriter()
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for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
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reader = PdfReader(pdf_file)
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for page in reader.pages:
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writer.add_page(page)
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with open("merged.pdf", "wb") as output:
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writer.write(output)
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```
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#### Split PDF
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```python
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reader = PdfReader("input.pdf")
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for i, page in enumerate(reader.pages):
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writer = PdfWriter()
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writer.add_page(page)
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with open(f"page_{i+1}.pdf", "wb") as output:
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writer.write(output)
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```
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#### Extract Metadata
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```python
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reader = PdfReader("document.pdf")
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meta = reader.metadata
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print(f"Title: {meta.title}")
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print(f"Author: {meta.author}")
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print(f"Subject: {meta.subject}")
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print(f"Creator: {meta.creator}")
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```
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#### Rotate Pages
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```python
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reader = PdfReader("input.pdf")
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writer = PdfWriter()
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page = reader.pages[0]
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page.rotate(90) # Rotate 90 degrees clockwise
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writer.add_page(page)
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with open("rotated.pdf", "wb") as output:
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writer.write(output)
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```
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### pdfplumber — extraccao de texto e tabelas
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#### Extract Text with Layout
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```python
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import pdfplumber
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with pdfplumber.open("document.pdf") as pdf:
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for page in pdf.pages:
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text = page.extract_text()
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print(text)
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```
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#### Extract Tables
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```python
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with pdfplumber.open("document.pdf") as pdf:
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for i, page in enumerate(pdf.pages):
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tables = page.extract_tables()
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for j, table in enumerate(tables):
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print(f"Table {j+1} on page {i+1}:")
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for row in table:
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print(row)
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```
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#### Advanced Table Extraction
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```python
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import pandas as pd
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with pdfplumber.open("document.pdf") as pdf:
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all_tables = []
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for page in pdf.pages:
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tables = page.extract_tables()
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for table in tables:
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if table: # Check if table is not empty
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df = pd.DataFrame(table[1:], columns=table[0])
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all_tables.append(df)
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# Combine all tables
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if all_tables:
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combined_df = pd.concat(all_tables, ignore_index=True)
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combined_df.to_excel("extracted_tables.xlsx", index=False)
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```
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### reportlab — criacao de PDFs
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#### Basic PDF Creation
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```python
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from reportlab.lib.pagesizes import letter
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from reportlab.pdfgen import canvas
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c = canvas.Canvas("hello.pdf", pagesize=letter)
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width, height = letter
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# Add text
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c.drawString(100, height - 100, "Hello World!")
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c.drawString(100, height - 120, "This is a PDF created with reportlab")
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# Add a line
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c.line(100, height - 140, 400, height - 140)
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# Save
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c.save()
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```
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#### Create PDF with Multiple Pages
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```python
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from reportlab.lib.pagesizes import letter
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from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, PageBreak
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from reportlab.lib.styles import getSampleStyleSheet
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doc = SimpleDocTemplate("report.pdf", pagesize=letter)
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styles = getSampleStyleSheet()
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story = []
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# Add content
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title = Paragraph("Report Title", styles['Title'])
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story.append(title)
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story.append(Spacer(1, 12))
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body = Paragraph("This is the body of the report. " * 20, styles['Normal'])
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story.append(body)
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story.append(PageBreak())
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# Page 2
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story.append(Paragraph("Page 2", styles['Heading1']))
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story.append(Paragraph("Content for page 2", styles['Normal']))
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# Build PDF
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doc.build(story)
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```
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#### Subscripts and Superscripts
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**Importante**: nunca usar caracteres Unicode subscript/superscript (subscript: 0-9, superscript: 0-9) em PDFs ReportLab. As fontes built-in nao incluem estes glifos, resultando em caixas pretas.
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Usar as tags XML do ReportLab em objectos Paragraph:
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```python
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from reportlab.platypus import Paragraph
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from reportlab.lib.styles import getSampleStyleSheet
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styles = getSampleStyleSheet()
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# Subscripts: use <sub> tag
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chemical = Paragraph("H<sub>2</sub>O", styles['Normal'])
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# Superscripts: use <super> tag
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squared = Paragraph("x<super>2</super> + y<super>2</super>", styles['Normal'])
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```
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Para texto desenhado com canvas (nao Paragraph), ajustar manualmente o tamanho da fonte e posicao.
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## Ferramentas de linha de comandos
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### pdftotext (poppler-utils)
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```bash
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# Extract text
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pdftotext input.pdf output.txt
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# Extract text preserving layout
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pdftotext -layout input.pdf output.txt
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# Extract specific pages
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pdftotext -f 1 -l 5 input.pdf output.txt # Pages 1-5
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```
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### qpdf
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```bash
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# Merge PDFs
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qpdf --empty --pages file1.pdf file2.pdf -- merged.pdf
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# Split pages
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qpdf input.pdf --pages . 1-5 -- pages1-5.pdf
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qpdf input.pdf --pages . 6-10 -- pages6-10.pdf
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# Rotate pages
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qpdf input.pdf output.pdf --rotate=+90:1 # Rotate page 1 by 90 degrees
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# Remove password
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qpdf --password=mypassword --decrypt encrypted.pdf decrypted.pdf
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```
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### pdftk (if available)
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```bash
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# Merge
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pdftk file1.pdf file2.pdf cat output merged.pdf
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# Split
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pdftk input.pdf burst
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# Rotate
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pdftk input.pdf rotate 1east output rotated.pdf
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```
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## Tarefas comuns
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### Extrair texto de PDFs digitalizados (OCR)
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```python
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# Requires: pip install pytesseract pdf2image
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import pytesseract
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from pdf2image import convert_from_path
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# Convert PDF to images
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images = convert_from_path('scanned.pdf')
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# OCR each page
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text = ""
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for i, image in enumerate(images):
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text += f"Page {i+1}:\n"
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text += pytesseract.image_to_string(image)
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text += "\n\n"
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print(text)
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```
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### Adicionar watermark
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```python
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from pypdf import PdfReader, PdfWriter
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# Create watermark (or load existing)
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watermark = PdfReader("watermark.pdf").pages[0]
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# Apply to all pages
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reader = PdfReader("document.pdf")
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writer = PdfWriter()
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for page in reader.pages:
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page.merge_page(watermark)
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writer.add_page(page)
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with open("watermarked.pdf", "wb") as output:
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writer.write(output)
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```
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### Extrair imagens
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```bash
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# Using pdfimages (poppler-utils)
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pdfimages -j input.pdf output_prefix
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# This extracts all images as output_prefix-000.jpg, output_prefix-001.jpg, etc.
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```
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### Proteccao por password
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```python
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from pypdf import PdfReader, PdfWriter
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reader = PdfReader("input.pdf")
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writer = PdfWriter()
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for page in reader.pages:
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writer.add_page(page)
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# Add password
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writer.encrypt("userpassword", "ownerpassword")
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with open("encrypted.pdf", "wb") as output:
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writer.write(output)
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```
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## Referencia rapida
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| Tarefa | Melhor ferramenta | Comando/codigo |
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|--------|-------------------|----------------|
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| Merge PDFs | pypdf | `writer.add_page(page)` |
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| Split PDFs | pypdf | One page per file |
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| Extrair texto | pdfplumber | `page.extract_text()` |
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| Extrair tabelas | pdfplumber | `page.extract_tables()` |
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| Criar PDFs | reportlab | Canvas or Platypus |
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| Merge CLI | qpdf | `qpdf --empty --pages ...` |
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| OCR scanned PDFs | pytesseract | Convert to image first |
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| Preencher formularios | pypdf ou annotations | Ver seccao abaixo |
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---
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## Preenchimento de formularios
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**Obrigatorio: seguir estes passos por ordem. Nao saltar para codigo directamente.**
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Primeiro verificar se o PDF tem campos preenchíveis. Executar a partir da pasta de scripts desta skill:
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`python scripts/check_fillable_fields.py <file.pdf>`
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Consoante o resultado, seguir a seccao "Campos preenchíveis" ou "Campos nao preenchíveis".
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### Campos preenchíveis
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Se o PDF tiver campos de formulario nativos:
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1. Extrair informacao dos campos:
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`python scripts/extract_form_field_info.py <input.pdf> <field_info.json>`
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O JSON resultante contem campos com esta estrutura:
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```json
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[
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{
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"field_id": "(ID unico do campo)",
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"page": "(numero da pagina, 1-based)",
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"rect": "[left, bottom, right, top]",
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"type": "text | checkbox | radio_group | choice"
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}
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]
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```
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Para **checkboxes**: propriedades `checked_value` e `unchecked_value`.
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Para **radio groups**: lista `radio_options` com `value` e `rect`.
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Para **choice fields**: lista `choice_options` com `value` e `text`.
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2. Converter PDF para imagens para analise visual:
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`python scripts/convert_pdf_to_images.py <file.pdf> <output_directory>`
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Analisar as imagens para determinar o proposito de cada campo.
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3. Criar `field_values.json`:
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```json
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[
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{
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"field_id": "last_name",
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"description": "Apelido do utilizador",
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"page": 1,
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"value": "Silva"
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},
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{
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"field_id": "Checkbox12",
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"description": "Checkbox para maiores de 18",
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"page": 1,
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"value": "/On"
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}
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]
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```
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4. Preencher:
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`python scripts/fill_fillable_fields.py <input.pdf> <field_values.json> <output.pdf>`
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### Campos nao preenchíveis
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Se o PDF nao tiver campos nativos, usar anotacoes de texto. Tentar primeiro extraccao por estrutura (mais preciso), depois estimativa visual como fallback.
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#### Passo 1: extraccao por estrutura
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`python scripts/extract_form_structure.py <input.pdf> form_structure.json`
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Extrai labels de texto, linhas horizontais e checkboxes com coordenadas exactas.
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**Se form_structure.json tiver labels significativos** -> usar abordagem A (estrutura).
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**Se o PDF for digitalizado/imagem** -> usar abordagem B (visual).
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#### Abordagem A: coordenadas por estrutura (preferida)
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Analisar form_structure.json e identificar:
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- **Label groups**: elementos de texto adjacentes que formam um label
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- **Row structure**: labels com `top` similar estao na mesma linha
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- **Field columns**: areas de entrada comecam apos o label (x0 = label.x1 + gap)
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- **Checkboxes**: usar coordenadas directamente do JSON
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Criar fields.json com `pdf_width`/`pdf_height`:
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```json
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{
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"pages": [
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{"page_number": 1, "pdf_width": 612, "pdf_height": 792}
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],
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"form_fields": [
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{
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"page_number": 1,
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"description": "Campo apelido",
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"field_label": "Apelido",
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"label_bounding_box": [43, 63, 87, 73],
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"entry_bounding_box": [92, 63, 260, 79],
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"entry_text": {"text": "Silva", "font_size": 10}
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}
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]
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}
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```
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#### Abordagem B: estimativa visual (fallback)
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1. Converter PDF para imagens:
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`python scripts/convert_pdf_to_images.py <input.pdf> <images_dir/>`
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2. Identificar campos e posicoes aproximadas nas imagens.
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3. Refinar com zoom (ImageMagick):
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```bash
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magick <page_image> -crop <width>x<height>+<x>+<y> +repage <crop_output.png>
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```
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Converter coordenadas do crop de volta para coordenadas da imagem completa:
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- full_x = crop_x + crop_offset_x
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- full_y = crop_y + crop_offset_y
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4. Criar fields.json com `image_width`/`image_height`.
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#### Abordagem hibrida
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Quando a extraccao por estrutura funciona para a maioria dos campos mas falta alguns:
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1. Usar abordagem A para campos detectados
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2. Usar abordagem B para campos em falta
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3. Converter todas as coordenadas para PDF:
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- pdf_x = image_x * (pdf_width / image_width)
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- pdf_y = image_y * (pdf_height / image_height)
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4. Usar sistema de coordenadas unico com `pdf_width`/`pdf_height`
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#### Validacao e preenchimento
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Validar bounding boxes antes de preencher:
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`python scripts/check_bounding_boxes.py fields.json`
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Preencher o formulario:
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`python scripts/fill_pdf_form_with_annotations.py <input.pdf> fields.json <output.pdf>`
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Verificar resultado:
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`python scripts/convert_pdf_to_images.py <output.pdf> <verify_images/>`
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Criar imagem de validacao com bounding boxes sobrepostas:
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`python scripts/create_validation_image.py <page_number> <fields.json> <input_image> <output_image>`
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---
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## Referencia avancada
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### pypdfium2 — rendering rapido
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```python
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import pypdfium2 as pdfium
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from PIL import Image
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# Load PDF
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pdf = pdfium.PdfDocument("document.pdf")
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# Render page to image
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page = pdf[0]
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bitmap = page.render(scale=2.0, rotation=0)
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img = bitmap.to_pil()
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img.save("page_1.png", "PNG")
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# Process multiple pages
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for i, page in enumerate(pdf):
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bitmap = page.render(scale=1.5)
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img = bitmap.to_pil()
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img.save(f"page_{i+1}.jpg", "JPEG", quality=90)
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```
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### pdfplumber — funcionalidades avancadas
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#### Texto com coordenadas precisas
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```python
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import pdfplumber
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with pdfplumber.open("document.pdf") as pdf:
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page = pdf.pages[0]
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# Extract all text with coordinates
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chars = page.chars
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for char in chars[:10]:
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print(f"Char: '{char['text']}' at x:{char['x0']:.1f} y:{char['y0']:.1f}")
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# Extract text by bounding box (left, top, right, bottom)
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bbox_text = page.within_bbox((100, 100, 400, 200)).extract_text()
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```
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#### Tabelas complexas com settings customizados
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```python
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import pdfplumber
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import pandas as pd
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with pdfplumber.open("complex_table.pdf") as pdf:
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page = pdf.pages[0]
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table_settings = {
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"vertical_strategy": "lines",
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"horizontal_strategy": "lines",
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"snap_tolerance": 3,
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"intersection_tolerance": 15
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}
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tables = page.extract_tables(table_settings)
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# Visual debugging for table extraction
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img = page.to_image(resolution=150)
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img.save("debug_layout.png")
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```
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### reportlab — relatorios profissionais com tabelas
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```python
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from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph
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from reportlab.lib.styles import getSampleStyleSheet
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from reportlab.lib import colors
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data = [
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['Produto', 'Q1', 'Q2', 'Q3', 'Q4'],
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['Widgets', '120', '135', '142', '158'],
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['Gadgets', '85', '92', '98', '105']
|
|
]
|
|
|
|
doc = SimpleDocTemplate("report.pdf")
|
|
elements = []
|
|
|
|
styles = getSampleStyleSheet()
|
|
title = Paragraph("Relatorio Trimestral de Vendas", styles['Title'])
|
|
elements.append(title)
|
|
|
|
table = Table(data)
|
|
table.setStyle(TableStyle([
|
|
('BACKGROUND', (0, 0), (-1, 0), colors.grey),
|
|
('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
|
|
('ALIGN', (0, 0), (-1, -1), 'CENTER'),
|
|
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
|
|
('FONTSIZE', (0, 0), (-1, 0), 14),
|
|
('BOTTOMPADDING', (0, 0), (-1, 0), 12),
|
|
('BACKGROUND', (0, 1), (-1, -1), colors.beige),
|
|
('GRID', (0, 0), (-1, -1), 1, colors.black)
|
|
]))
|
|
elements.append(table)
|
|
|
|
doc.build(elements)
|
|
```
|
|
|
|
### JavaScript — pdf-lib (criacao e modificacao)
|
|
|
|
#### Load and Manipulate Existing PDF
|
|
```javascript
|
|
import { PDFDocument } from 'pdf-lib';
|
|
import fs from 'fs';
|
|
|
|
async function manipulatePDF() {
|
|
const existingPdfBytes = fs.readFileSync('input.pdf');
|
|
const pdfDoc = await PDFDocument.load(existingPdfBytes);
|
|
|
|
const pageCount = pdfDoc.getPageCount();
|
|
const newPage = pdfDoc.addPage([600, 400]);
|
|
newPage.drawText('Added by pdf-lib', { x: 100, y: 300, size: 16 });
|
|
|
|
const pdfBytes = await pdfDoc.save();
|
|
fs.writeFileSync('modified.pdf', pdfBytes);
|
|
}
|
|
```
|
|
|
|
#### Advanced Merge and Split
|
|
```javascript
|
|
import { PDFDocument } from 'pdf-lib';
|
|
import fs from 'fs';
|
|
|
|
async function mergePDFs() {
|
|
const mergedPdf = await PDFDocument.create();
|
|
|
|
const pdf1 = await PDFDocument.load(fs.readFileSync('doc1.pdf'));
|
|
const pdf2 = await PDFDocument.load(fs.readFileSync('doc2.pdf'));
|
|
|
|
const pdf1Pages = await mergedPdf.copyPages(pdf1, pdf1.getPageIndices());
|
|
pdf1Pages.forEach(page => mergedPdf.addPage(page));
|
|
|
|
const pdf2Pages = await mergedPdf.copyPages(pdf2, [0, 2, 4]);
|
|
pdf2Pages.forEach(page => mergedPdf.addPage(page));
|
|
|
|
fs.writeFileSync('merged.pdf', await mergedPdf.save());
|
|
}
|
|
```
|
|
|
|
### Operacoes avancadas CLI
|
|
|
|
#### poppler-utils
|
|
```bash
|
|
# Text with bounding box coordinates
|
|
pdftotext -bbox-layout document.pdf output.xml
|
|
|
|
# High-resolution PNG conversion
|
|
pdftoppm -png -r 300 document.pdf output_prefix
|
|
|
|
# Specific page range with high resolution
|
|
pdftoppm -png -r 600 -f 1 -l 3 document.pdf high_res_pages
|
|
|
|
# Extract all embedded images with metadata
|
|
pdfimages -j -p document.pdf page_images
|
|
|
|
# List image info without extracting
|
|
pdfimages -list document.pdf
|
|
```
|
|
|
|
#### qpdf avancado
|
|
```bash
|
|
# Split PDF into groups of pages
|
|
qpdf --split-pages=3 input.pdf output_group_%02d.pdf
|
|
|
|
# Complex page ranges from multiple PDFs
|
|
qpdf --empty --pages doc1.pdf 1-3 doc2.pdf 5-7 doc3.pdf 2,4 -- combined.pdf
|
|
|
|
# Optimize for web (linearize)
|
|
qpdf --linearize input.pdf optimized.pdf
|
|
|
|
# Repair corrupted PDF
|
|
qpdf --check input.pdf
|
|
qpdf --fix-qdf damaged.pdf repaired.pdf
|
|
|
|
# Advanced encryption with permissions
|
|
qpdf --encrypt user_pass owner_pass 256 --print=none --modify=none -- input.pdf encrypted.pdf
|
|
|
|
# Check encryption status
|
|
qpdf --show-encryption encrypted.pdf
|
|
```
|
|
|
|
### Processamento em lote com error handling
|
|
|
|
```python
|
|
import os
|
|
import glob
|
|
from pypdf import PdfReader, PdfWriter
|
|
import logging
|
|
|
|
logging.basicConfig(level=logging.INFO)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
def batch_process_pdfs(input_dir, operation='merge'):
|
|
pdf_files = glob.glob(os.path.join(input_dir, "*.pdf"))
|
|
|
|
if operation == 'merge':
|
|
writer = PdfWriter()
|
|
for pdf_file in pdf_files:
|
|
try:
|
|
reader = PdfReader(pdf_file)
|
|
for page in reader.pages:
|
|
writer.add_page(page)
|
|
logger.info(f"Processed: {pdf_file}")
|
|
except Exception as e:
|
|
logger.error(f"Failed to process {pdf_file}: {e}")
|
|
continue
|
|
|
|
with open("batch_merged.pdf", "wb") as output:
|
|
writer.write(output)
|
|
|
|
elif operation == 'extract_text':
|
|
for pdf_file in pdf_files:
|
|
try:
|
|
reader = PdfReader(pdf_file)
|
|
text = ""
|
|
for page in reader.pages:
|
|
text += page.extract_text()
|
|
|
|
output_file = pdf_file.replace('.pdf', '.txt')
|
|
with open(output_file, 'w', encoding='utf-8') as f:
|
|
f.write(text)
|
|
logger.info(f"Extracted text from: {pdf_file}")
|
|
except Exception as e:
|
|
logger.error(f"Failed to extract text from {pdf_file}: {e}")
|
|
continue
|
|
```
|
|
|
|
### Cropping avancado
|
|
|
|
```python
|
|
from pypdf import PdfWriter, PdfReader
|
|
|
|
reader = PdfReader("input.pdf")
|
|
writer = PdfWriter()
|
|
|
|
page = reader.pages[0]
|
|
page.mediabox.left = 50
|
|
page.mediabox.bottom = 50
|
|
page.mediabox.right = 550
|
|
page.mediabox.top = 750
|
|
|
|
writer.add_page(page)
|
|
with open("cropped.pdf", "wb") as output:
|
|
writer.write(output)
|
|
```
|
|
|
|
### Gestao de memoria para PDFs grandes
|
|
|
|
```python
|
|
def process_large_pdf(pdf_path, chunk_size=10):
|
|
reader = PdfReader(pdf_path)
|
|
total_pages = len(reader.pages)
|
|
|
|
for start_idx in range(0, total_pages, chunk_size):
|
|
end_idx = min(start_idx + chunk_size, total_pages)
|
|
writer = PdfWriter()
|
|
|
|
for i in range(start_idx, end_idx):
|
|
writer.add_page(reader.pages[i])
|
|
|
|
with open(f"chunk_{start_idx//chunk_size}.pdf", "wb") as output:
|
|
writer.write(output)
|
|
```
|
|
|
|
## Troubleshooting
|
|
|
|
### PDFs encriptados
|
|
```python
|
|
from pypdf import PdfReader
|
|
|
|
try:
|
|
reader = PdfReader("encrypted.pdf")
|
|
if reader.is_encrypted:
|
|
reader.decrypt("password")
|
|
except Exception as e:
|
|
print(f"Failed to decrypt: {e}")
|
|
```
|
|
|
|
### PDFs corrompidos
|
|
```bash
|
|
qpdf --check corrupted.pdf
|
|
qpdf --replace-input corrupted.pdf
|
|
```
|
|
|
|
### Falha na extraccao de texto (fallback para OCR)
|
|
```python
|
|
import pytesseract
|
|
from pdf2image import convert_from_path
|
|
|
|
def extract_text_with_ocr(pdf_path):
|
|
images = convert_from_path(pdf_path)
|
|
text = ""
|
|
for i, image in enumerate(images):
|
|
text += pytesseract.image_to_string(image)
|
|
return text
|
|
```
|
|
|
|
## Dicas de performance
|
|
|
|
1. **PDFs grandes**: usar streaming em vez de carregar tudo em memoria; `qpdf --split-pages` para dividir
|
|
2. **Extraccao de texto**: `pdftotext -bbox-layout` e o mais rapido; pdfplumber para tabelas
|
|
3. **Extraccao de imagens**: `pdfimages` e muito mais rapido que rendering de paginas
|
|
4. **Preenchimento de formularios**: pdf-lib mantem melhor a estrutura do formulario
|
|
5. **Memoria**: processar paginas individualmente com pypdfium2 para documentos grandes
|
|
|
|
---
|
|
|
|
## Integracao Descomplicar
|
|
|
|
### Caminhos frequentes para PDFs
|
|
|
|
| Localizacao | Caminho |
|
|
|-------------|---------|
|
|
| Documentos empresa | `/media/ealmeida/Dados/GDrive/Cloud/Descomplicar/` |
|
|
| Propostas | `/media/ealmeida/Dados/Hub/03-Propostas/` |
|
|
| Arquivo clientes | `/media/ealmeida/Dados/GDrive/Arquivo_de_Clientes/` |
|
|
| Knowledge Base | `/media/ealmeida/Dados/Hub/06-Operacoes/Knowledge-Base/PDFs/` |
|
|
| Backups | `/media/ealmeida/Dados/GDrive/Backups/` |
|
|
| Temporarios | `~/.claude-work/` (limpar ao concluir) |
|
|
|
|
### MCPs relevantes
|
|
|
|
- **mcp__filesystem__read_file** / **write_file**: ler e escrever PDFs locais
|
|
- **mcp__filesystem__search_files**: encontrar PDFs no sistema
|
|
- **mcp__google-workspace__drive_search_files**: encontrar PDFs no Google Drive
|
|
- **mcp__google-workspace__drive_read_file_content**: ler conteudo de ficheiros no Drive
|
|
- **mcp__google-workspace__drive_upload_file**: enviar PDFs processados para o Drive
|
|
|
|
### Workflow tipico Descomplicar
|
|
|
|
1. Localizar PDF (filesystem ou Google Drive)
|
|
2. Descarregar para `~/.claude-work/` se necessario
|
|
3. Processar (extrair, merge, split, OCR, etc.)
|
|
4. Guardar resultado no destino final
|
|
5. Limpar temporarios de `~/.claude-work/`
|
|
|
|
---
|
|
|
|
## Licencas das bibliotecas
|
|
|
|
- **pypdf**: BSD | **pdfplumber**: MIT | **pypdfium2**: Apache/BSD | **reportlab**: BSD
|
|
- **poppler-utils**: GPL-2 | **qpdf**: Apache | **pdf-lib**: MIT | **pdfjs-dist**: Apache
|
|
|
|
---
|
|
**Versao**: 1.0.0 | **Autor**: Descomplicar®
|