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:
python# 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):
pythonBUSINESS_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):
pythonMODERN_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):
pythonACADEMIC_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
pythondef 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
pythondef 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
pythonimport 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
pythonfrom 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
bash# 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
pythonfrom 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
pythondef 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
bash# 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
