sn-da-non-spreadsheet-analysis

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

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