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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
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-non-spreadsheet-analysis -a cursor -y
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）。
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）。
End-to-end workflow for Word, PDF, and PPT document parsing. Each format has specific parsing pitfalls — follow the format-specific sub-skill exactly.
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_pathis 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.
| 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.
Follow the sub-skill's extraction pattern. For all formats:
caption.pyAfter 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"
for page in doc, for slide in prs.slides, for para in doc.paragraphscaption.py for OCRcaption.pypytesseract or easyocr as primary OCR — they are not installed; use caption.pyWhen 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。"
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