Sn Da Non Spreadsheet Analysis Agent Skill

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）。

9k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

4851

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill sn-da-non-spreadsheet-analysis

The instruction itself

11 sections, as written by the author

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

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How to use it

Copy the folder

Take opensensenova/sn-da-non-spreadsheet-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.