word-formatter

name word-formatter description Use when user has a messy Word document or plain text that needs professional formatting, auto-layout, and intelligent image generation. Triggers on requests to format, beautify, or reorganize documents. Word Formatter Transform messy documents into professionally formatted Word/PDF files with intelligent layout and auto-generated images. When to Use User provides a Word document (.docx) or text file needing cleanup Request to "format", "beautify", "reorganize" a document Need to add visuals/charts to plain text content Converting notes/drafts into professional documents Workflow 1. Analyze Document → 2. User Chooses Level → 3. Format → 4. Generate Images → 5. Output Step 1: Analyze Document Read the input file and detect: # Content type detection keywords CONTENT_TYPES = { "meeting_notes" : [ "会议" , "议程" , "参会" , "决议" , "行动项" , "meeting" , "agenda" ], "report" : [ "报告" , "总结" , "分析" , "数据" , "report" , "analysis" , "summary" ], "study_notes" : [ "笔记" , "学习" , "知识点" , "概念" , "notes" , "learning" ], "proposal" : [ "方案" , "计划" , "目标" , "预算" , "proposal" , "plan" , "budget" ] } # Style mapping STYLE_MAP = { "meeting_notes" : "business_formal" , "report" : "business_formal" , "study_notes" : "modern_minimal" , "proposal" : "academic" } Present analysis to user: 检测结果: - 文档类型: [类型] - 建议风格: [风格] - 字数: [X] 字 - 检测到数据: [是/否] Step 2: User Chooses Processing Level Ask user to select: 级别 说明 仅排版 保持原文内容，只调整格式样式 轻度重组 添加小标题、调整段落、补充过渡 深度重构 可重写内容、优化表达、补充逻辑 Step 3: Apply Formatting Style Definitions Business Formal (商务正式): BUSINESS_STYLE = { "title_font" : "Microsoft YaHei" , "title_size" : 22 , "heading1_size" : 16 , "heading2_size" : 14 , "body_size" : 11 , "line_spacing" : 1.5 , "colors" : { "primary" : "#1a365d" , # Deep blue "accent" : "#2b6cb0" , # Medium blue "text" : "#2d3748" # Dark gray } } Modern Minimal (简约现代): MODERN_STYLE = { "title_font" : "PingFang SC" , "title_size" : 24 , "heading1_size" : 18 , "heading2_size" : 14 , "body_size" : 11 , "line_spacing" : 1.8 , "colors" : { "primary" : "#1a202c" , # Near black "accent" : "#4a5568" , # Gray "text" : "#2d3748" } } Academic (学术风格): ACADEMIC_STYLE = { "title_font" : "SimSun" , "title_size" : 18 , "heading1_size" : 15 , "heading2_size" : 13 , "body_size" : 12 , "line_spacing" : 1.5 , "colors" : { "primary" : "#1a202c" , "accent" : "#4a5568" , "text" : "#000000" } } Document Structure def structure_document ( content, level ): """ Structure document based on processing level """ if level == "format_only" : # Keep original structure, apply styles return apply_styles(content) elif level == "light_restructure" : # Add headings, reorder paragraphs, add transitions sections = detect_sections(content) sections = add_subheadings(sections) sections = add_transitions(sections) return apply_styles(sections) elif level == "deep_restructure" : # Rewrite unclear parts, add logic, improve flow sections = detect_sections(content) sections = rewrite_unclear(sections) sections = add_missing_logic(sections) sections = optimize_expression(sections) return apply_styles(sections) Step 4: Generate Images Image Decision Logic def decide_images ( content ): """ Decide what images to generate based on content """ images = [] # Check for data → Charts if contains_data(content): data_sections = extract_data(content) for section in data_sections: chart_type = suggest_chart_type(section) images.append({ "type" : "chart" , "chart_type" : chart_type, "data" : section }) # Check for concepts → AI illustrations concepts = extract_key_concepts(content) if concepts: images.append({ "type" : "illustration" , "concept" : concepts[ 0 ], "style" : get_document_style() }) # Add cover image images.append({ "type" : "cover" , "title" : get_document_title(content), "style" : get_document_style() }) return images Chart Generation import matplotlib.pyplot as plt import matplotlib matplotlib.rcParams[ 'font.sans-serif' ] = [ 'PingFang SC' , 'Microsoft YaHei' ] def generate_chart ( data, chart_type, style ): """ Generate chart matching document style """ colors = style[ "colors" ] fig, ax = plt.subplots(figsize=( 10 , 6 )) if chart_type == "bar" : ax.bar(data[ "labels" ], data[ "values" ], color=colors[ "primary" ]) elif chart_type == "pie" : ax.pie(data[ "values" ], labels=data[ "labels" ], colors=[colors[ "primary" ], colors[ "accent" ], "#e2e8f0" ]) elif chart_type == "line" : ax.plot(data[ "x" ], data[ "y" ], color=colors[ "primary" ], linewidth= 2 ) ax.set_title(data[ "title" ], fontsize= 14 , color=colors[ "text" ]) return fig AI Illustration Generation Use the generate-image skill: Prompt template for business style: "Professional business illustration of [concept], flat design, corporate blue color scheme, minimalist, clean background, vector style" Prompt template for modern style: "Modern minimalist illustration of [concept], geometric shapes, black and white with accent color, clean lines, abstract, professional" Prompt template for academic style: "Educational diagram illustrating [concept], clean and simple, textbook style, labeled components, neutral colors" Image Confirmation Present image plan to user: 配图方案: 1. 封面图: [主题描述] 2. 数据图表: [图表类型] - [数据来源段落] 3. 概念插图: [概念名称] 请选择: A) 全部生成 B) 部分修改 (请指定) C) 跳过配图 Step 5: Output Files Generate Word Document from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH def create_word_document ( content, images, style ): doc = Document() # Set page margins for section in doc.sections: section.top_margin = Inches( 1 ) section.bottom_margin = Inches( 1 ) section.left_margin = Inches( 1.25 ) section.right_margin = Inches( 1.25 ) # Add cover image if exists if images.get( "cover" ): doc.add_picture(images[ "cover" ], width=Inches( 6 )) doc.add_page_break() # Add content with styles for element in content: if element[ "type" ] == "title" : p = doc.add_heading(element[ "text" ], level= 0 ) p.alignment = WD_ALIGN_PARAGRAPH.CENTER elif element[ "type" ] == "heading1" : doc.add_heading(element[ "text" ], level= 1 ) elif element[ "type" ] == "heading2" : doc.add_heading(element[ "text" ], level= 2 ) elif element[ "type" ] == "paragraph" : p = doc.add_paragraph(element[ "text" ]) p.paragraph_format.line_spacing = style[ "line_spacing" ] elif element[ "type" ] == "image" : doc.add_picture(element[ "path" ], width=Inches( 5 )) return doc Convert to PDF # Using LibreOffice (cross-platform) libreoffice --headless --convert-to pdf --outdir [output_dir] [docx_file] # Or using docx2pdf (requires MS Word on Windows/Mac) # pip install docx2pdf from docx2pdf import convert def export_pdf ( docx_path ): pdf_path = docx_path.replace( '.docx' , '.pdf' ) convert(docx_path, pdf_path) return pdf_path Output Naming def get_output_paths ( input_path ): base = os.path.splitext(input_path)[ 0 ] return { "docx" : f" {base} _formatted.docx" , "pdf" : f" {base} _formatted.pdf" } Complete Interaction Flow User: /word-formatter /path/to/messy_document.docx Claude: 正在分析文档... 检测结果: - 文档类型: 工作报告 - 建议风格: 商务正式 - 字数: 2,350 字 - 检测到数据: 是 (3处数据可视化) 请选择处理程度: A) 仅排版 - 保持原文，调整格式 B) 轻度重组 - 添加小标题，优化结构 C) 深度重构 - 可重写内容，优化表达 User: B Claude: 好的，将进行轻度重组。正在处理... 配图方案: 1. 封面图: 商务风格的工作报告封面 2. 柱状图: 季度销售数据对比 3. 饼图: 市场份额分布 4. 概念插图: 增长策略示意图 请选择: A) 全部生成 B) 部分修改 C) 跳过配图 User: A Claude: 正在生成配图... [生成图片中...] 完成！输出文件: - /path/to/messy_document_formatted.docx - /path/to/messy_document_formatted.pdf 需要调整吗？ Dependencies # Required Python packages pip install python-docx matplotlib docx2pdf pillow # For PDF conversion (alternative to docx2pdf) # macOS: brew install --cask libreoffice # Ubuntu: sudo apt install libreoffice Error Handling 错误 处理方式 文件不存在 提示用户检查路径 格式不支持 仅支持 .docx, .txt, .md 图片生成失败 跳过该图片，继续处理 PDF转换失败 仅输出Word，提示安装依赖 Tips 输入文档越结构化，排版效果越好 深度重构模式下，Claude会尝试改善文档逻辑 配图会自动匹配文档风格的配色方案 可以多次调用进行迭代优化