microsoft-word

name microsoft-word description Create, edit, and manipulate Word documents (.docx) programmatically. Use when someone asks to "generate a Word document", "create a report in docx", "mail merge", "fill Word template", "extract text from Word", "convert markdown to Word", "add tables to Word", or "automate document generation". Covers python-docx for file manipulation, docxtpl for templates, and Microsoft Graph API for cloud documents. license Apache-2.0 compatibility python-docx (Python 3.8+), docxtpl for templates, Graph API for SharePoint/OneDrive docs. metadata {"author":"terminal-skills","version":"1.0.0","category":"development","tags":["microsoft-word","docx","document-generation","templates","mail-merge"]} Microsoft Word Overview This skill helps AI agents create and manipulate Word documents programmatically. It covers generating .docx files with python-docx, template-based document generation with docxtpl, mail merge, and managing Word documents in SharePoint/OneDrive via Microsoft Graph API. Instructions Step 1: Generate Documents with python-docx pip install python-docx from docx import Document from docx.shared import Inches, Pt, Cm, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.table import WD_TABLE_ALIGNMENT from docx.enum.section import WD_ORIENT doc = Document() # --- Page setup --- section = doc.sections[ 0 ] section.page_width = Cm( 21 ) # A4 section.page_height = Cm( 29.7 ) section.top_margin = Cm( 2.5 ) section.bottom_margin = Cm( 2.5 ) section.left_margin = Cm( 3 ) section.right_margin = Cm( 2 ) # --- Title --- title = doc.add_heading( 'Quarterly Business Report' , level= 0 ) title.alignment = WD_ALIGN_PARAGRAPH.CENTER # --- Subtitle / metadata --- meta = doc.add_paragraph() meta.alignment = WD_ALIGN_PARAGRAPH.CENTER run = meta.add_run( 'Q1 2026 — Confidential' ) run.font.size = Pt( 12 ) run.font.color.rgb = RGBColor( 0x66 , 0x66 , 0x66 ) doc.add_page_break() # --- Table of Contents placeholder --- doc.add_heading( 'Table of Contents' , level= 1 ) doc.add_paragraph( '(Update field after opening in Word: Ctrl+A → F9)' ) doc.add_page_break() # --- Section with paragraphs --- doc.add_heading( 'Executive Summary' , level= 1 ) p = doc.add_paragraph() p.add_run( 'Revenue grew ' ).font.size = Pt( 11 ) p.add_run( '23% year-over-year' ).bold = True p.add_run( ', reaching $4.2M in Q1 2026. ' ) # Bullet list doc.add_heading( 'Key Highlights' , level= 2 ) for item in [ 'Revenue: $4.2M (+23% YoY)' , 'New customers: 847' , 'Churn rate: 2.1% (down from 3.4%)' ]: doc.add_paragraph(item, style= 'List Bullet' ) # Numbered list doc.add_heading( 'Priorities for Q2' , level= 2 ) for i, item in enumerate ([ 'Launch enterprise tier' , 'Hire 3 senior engineers' , 'Expand to EU market' ], 1 ): doc.add_paragraph(item, style= 'List Number' ) # --- Table --- doc.add_heading( 'Financial Summary' , level= 1 ) table = doc.add_table(rows= 1 , cols= 4 , style= 'Light Grid Accent 1' ) table.alignment = WD_TABLE_ALIGNMENT.CENTER # Header row headers = [ 'Metric' , 'Q1 2025' , 'Q1 2026' , 'Change' ] for i, header in enumerate (headers): cell = table.rows[ 0 ].cells[i] cell.text = header for paragraph in cell.paragraphs: for run in paragraph.runs: run.bold = True # Data rows data = [ [ 'Revenue' , '$3.4M' , '$4.2M' , '+23%' ], [ 'Customers' , '2,140' , '2,987' , '+40%' ], [ 'MRR' , '$283K' , '$350K' , '+24%' ], [ 'Churn' , '3.4%' , '2.1%' , '-38%' ], ] for row_data in data: row = table.add_row() for i, value in enumerate (row_data): row.cells[i].text = value # --- Image --- doc.add_heading( 'Growth Chart' , level= 2 ) doc.add_picture( '/path/to/chart.png' , width=Inches( 5.5 )) last_paragraph = doc.paragraphs[- 1 ] last_paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER # --- Header and Footer --- header = section.header header_para = header.paragraphs[ 0 ] header_para.text = 'Company Inc. — Confidential' header_para.alignment = WD_ALIGN_PARAGRAPH.RIGHT header_para.runs[ 0 ].font.size = Pt( 8 ) header_para.runs[ 0 ].font.color.rgb = RGBColor( 0x99 , 0x99 , 0x99 ) footer = section.footer footer_para = footer.paragraphs[ 0 ] footer_para.text = 'Page ' footer_para.alignment = WD_ALIGN_PARAGRAPH.CENTER # Save doc.save( 'quarterly-report-q1-2026.docx' ) Step 2: Template-Based Generation with docxtpl pip install docxtpl Create a Word template ( template.docx ) with Jinja2 tags: {{ company_name }} Date: {{ report_date }} Dear {{ client_name }}, Please find the summary of your account below: {% for item in line_items %} - {{ item.description }}: ${{ item.amount }} {% endfor %} Total: ${{ total }} {% if notes %} Notes: {{ notes }} {% endif %} from docxtpl import DocxTemplate doc = DocxTemplate( 'template.docx' ) context = { 'company_name' : 'Acme Corp' , 'report_date' : 'March 1, 2026' , 'client_name' : 'Sarah Chen' , 'line_items' : [ { 'description' : 'Web Development' , 'amount' : '12,500' }, { 'description' : 'UI/UX Design' , 'amount' : '4,200' }, { 'description' : 'Hosting (Annual)' , 'amount' : '1,800' }, ], 'total' : '18,500' , 'notes' : 'Payment due within 30 days.' , } doc.render(context) doc.save( 'invoice-sarah-chen.docx' ) Mail Merge (Batch Documents) import csv from docxtpl import DocxTemplate # Generate personalized documents from CSV with open ( 'clients.csv' ) as f: clients = list (csv.DictReader(f)) for client in clients: doc = DocxTemplate( 'offer-letter-template.docx' ) doc.render({ 'name' : client[ 'name' ], 'position' : client[ 'position' ], 'salary' : client[ 'salary' ], 'start_date' : client[ 'start_date' ], 'manager' : client[ 'manager' ], }) doc.save( f"offers/offer- {client[ 'name' ].replace( ' ' , '-' ).lower()} .docx" ) print ( f"Generated offer for {client[ 'name' ]} " ) Step 3: Read and Extract from Word from docx import Document doc = Document( 'report.docx' ) # Extract all text full_text = '\n' .join(para.text for para in doc.paragraphs) # Extract with structure for para in doc.paragraphs: if para.style.name.startswith( 'Heading' ): level = para.style.name.replace( 'Heading ' , '' ) print ( f" { '#' * int (level)} {para.text} " ) elif para.text.strip(): print (para.text) # Extract tables for table in doc.tables: for row in table.rows: row_data = [cell.text for cell in row.cells] print ( ' | ' .join(row_data)) # Extract images import io from docx.opc.constants import RELATIONSHIP_TYPE as RT for rel in doc.part.rels.values(): if "image" in rel.reltype: image_data = rel.target_part.blob with open ( f'extracted_ {rel.target_ref} ' , 'wb' ) as f: f.write(image_data) Step 4: Graph API (Cloud Documents) // Create Word document in OneDrive/SharePoint const newDoc = await graphClient . api ( `/drives/ ${driveId} /root:/Documents/report.docx:/content` ) . put (fs. readFileSync ( 'local-report.docx' )); // Convert Word to PDF const pdfStream = await graphClient . api ( `/drives/ ${driveId} /items/ ${itemId} /content?format=pdf` ) . getStream (); // Co-authoring: get temporary edit link const editLink = await graphClient . api ( `/drives/ ${driveId} /items/ ${itemId} /createLink` ) . post ({ type : 'edit' , scope : 'organization' }); // Opens in Word Online for collaborative editing Examples Example 1: Generate a quarterly business report as a Word document User prompt: "Create a Q4 2025 quarterly report for Athena SaaS. Revenue was $3.8M (up 18% YoY), 2,400 active customers, churn dropped to 1.9%. Include an executive summary, financial table, and priorities for Q1 2026." The agent will write a Python script using python-docx that creates an A4 document with 2.5cm margins, a centered title "Quarterly Business Report — Q4 2025", a subtitle "Athena SaaS — Confidential" in gray, then a page break. The Executive Summary section uses bold runs for key figures ("Revenue grew 18% year-over-year, reaching $3.8M"). A Financial Summary table with the Light Grid Accent 1 style has columns for Metric, Q4 2024, Q4 2025, and Change, populated with Revenue ($3.2M / $3.8M / +18%), Customers (1,980 / 2,400 / +21%), and Churn (2.6% / 1.9% / -27%). A Priorities section uses numbered list style with items like "Launch enterprise SSO by February" and "Expand sales team to cover EMEA." Headers and footers are set with "Athena SaaS — Confidential" and page numbers. The file saves as athena-q4-2025-report.docx . Example 2: Generate personalized offer letters from a CSV using mail merge User prompt: "I have an offer letter template at ./templates/offer-letter.docx and a CSV at ./data/new-hires.csv with columns name, position, salary, start_date, and manager. Generate individual offer letters for each person." The agent will write a Python script using docxtpl that loads the template and reads the CSV with csv.DictReader . For each row, it renders the template with the context {'name': 'Elena Vasquez', 'position': 'Senior Backend Engineer', 'salary': '$165,000', 'start_date': 'March 17, 2026', 'manager': 'David Park'} (and so on for each hire). Each rendered document saves to ./offers/offer-elena-vasquez.docx with the filename derived from the name field. The script prints a summary like "Generated 8 offer letters in ./offers/" and handles edge cases like missing fields by logging warnings instead of crashing. Guidelines Use templates (docxtpl) for repetitive documents — don't generate structure in code every time Set explicit font sizes and styles — don't rely on Normal style defaults Always specify measurements (Pt, Cm, Inches) — never use bare numbers Table styles: use built-in Word styles ( Light Grid Accent 1 , etc.) for consistent look For mail merge, load template once per batch, render per record Add alt text to images for accessibility Use doc.add_page_break() before major sections Test output in both Word and LibreOffice — rendering can differ For PDF conversion, Graph API's ?format=pdf is most reliable Keep templates in version control alongside code