sn-da-non-spreadsheet-analysis

name sn-da-non-spreadsheet-analysis description Word / PDF / PPT 文档解析与数据分析引擎。覆盖三类文件格式的全量提取、表格数值化、图表理解与跨文档汇总分析。**遇到以下任一情况就主动使用本 skill**：①用户上传或指定了 .docx / .doc / .pdf / .pptx / .ppt 文件并要求分析、提取或统计其中内容；②用户出现触发词：Word分析 / PDF解析 / PPT提取 / 文档分析 / 报告解析 / 幻灯片分析 / 发票提取 / 合同分析 / 文档统计 / 错别字 / 语病 / 字号检查 / 简历分析 / 多文档对比；③任务涉及从文档中提取表格、数值、图表、格式（颜色/高亮/字号）、组织架构、时间线等结构化信息。仅不用于：Excel/CSV 数据分析（使用 sn-da-excel-workflow）、纯图片分析（使用 sn-da-image-caption）。 Document Analysis Skill — Word / PDF / PPT End-to-end workflow for Word, PDF, and PPT document parsing. Each format has specific parsing pitfalls — follow the format-specific sub-skill exactly. Workflow Step 0 — Identify file type and input scope import os input_path = "/mnt/data/..." # from user # Detect single file vs directory (multi-file scenario) if os.path.isdir(input_path): all_files = [ os.path.join(input_path, f) for f in os.listdir(input_path) if f.lower().endswith(( '.docx' , '.doc' , '.pdf' , '.pptx' , '.ppt' )) ] print ( f"Found { len (all_files)} documents: {all_files} " ) else : all_files = [input_path] # Route by extension ext = os.path.splitext(all_files[ 0 ])[- 1 ].lower() print ( f"File type: {ext} " ) Critical rule : When input_path is a directory OR the user says "这些文件" / "所有文档", process every file and aggregate. Never stop at the first file. Step 1 — Load sub-skill by format Extension Sub-skill to load .docx / .doc capability/word-analysis/SKILL.md .pdf capability/pdf-analysis/SKILL.md .pptx / .ppt capability/ppt-analysis/SKILL.md read_file(path="<skills_root>/sn-da-non-spreadsheet-analysis/capability/<format>-analysis/SKILL.md") Load only the sub-skill you need — do not load all three at once. Step 2 — Parse and extract Follow the sub-skill's extraction pattern. For all formats: Full scan : iterate all pages/slides/paragraphs — never stop early Table extraction : get every table, not just the first one Image/chart detection : if a page/slide yields no text, treat it as image-based and call caption.py Step 3 — Answer with verification After extracting data, verify before answering: # For count/statistics questions: spot-check 3-5 items sample = result_list[: 3 ] print ( f"Sample check: {sample} " ) print ( f"Total count: { len (result_list)} " ) # For numeric calculations: print intermediate values print ( f"Max= {max_val} , Min= {min_val} , Range= {max_val - min_val} " ) # For unit-sensitive answers: always include the unit print ( f"Answer: {value} {unit} " ) # e.g., "475 千港元" not just "475" Universal Rules MUST DO Always iterate all pages/slides/paragraphs — for page in doc , for slide in prs.slides , for para in doc.paragraphs When input is a directory : collect and process all matching files, then aggregate results For scanned PDFs : detect empty text → call caption.py for OCR For image-only slides : text extraction returns empty → render slide as PNG → call caption.py For calculations : show intermediate values; confirm unit matches the question NEVER DO Do NOT use pytesseract or easyocr as primary OCR — they are not installed; use caption.py Do NOT use PIL pixel analysis to infer chart values — use vision model caption instead Do NOT stop at the first file, first page, or first table Do NOT guess content from filenames — always parse the actual file Do NOT output percentage when the question asks for absolute value (and vice versa) Caption Script (for image/chart content in any document) When a page, slide, or embedded image needs vision understanding, load the sn-da-image-caption skill first, then use its scripts/caption.py : read_file(path="<skills_root>/sn-da-image-caption/SKILL.md") import subprocess, json CAPTION = "/path/to/skills/sn-da-image-caption/scripts/caption.py" def caption_image ( image_path, prompt= None ): cmd = [ "python3" , CAPTION, image_path, "--json" ] if prompt: cmd += [ "--prompt" , prompt] result = subprocess.run(cmd, capture_output= True , text= True , timeout= 60 ) if result.returncode != 0 : raise RuntimeError( f"caption failed: {result.stderr[: 200 ]} " ) return json.loads(result.stdout)[ "description" ] # Example prompts by content type: # Table: "提取表格所有内容，Markdown 表格格式，保持行列结构，数值不四舍五入。" # Chart: "提取图表标题、坐标轴标签、每个数据点的数值。Markdown 表格输出。" # Diagram: "描述所有节点和连接关系。" Available sub-skills sn-da-non-spreadsheet-analysis/capability/word-analysis/SKILL.md — .docx/.doc sn-da-non-spreadsheet-analysis/capability/pdf-analysis/SKILL.md — .pdf sn-da-non-spreadsheet-analysis/capability/ppt-analysis/SKILL.md — .pptx/.ppt