zhukang
1c7abd81d1
- 310 registry:skill items, 1528 files, 7 categories (hub tags) - registry.json generated by @kortix/registry buildRegistry - nested SKILL.md in sn-da-* renamed SUBSKILL.md (no phantom skills) - provenance + licenses: SOURCES.md
144 lines
5.3 KiB
Markdown
144 lines
5.3 KiB
Markdown
---
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name: sn-da-non-spreadsheet-analysis
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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）。"
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---
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# Document Analysis Skill — Word / PDF / PPT
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End-to-end workflow for Word, PDF, and PPT document parsing. Each format has
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specific parsing pitfalls — follow the format-specific sub-skill exactly.
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---
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## Workflow
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### Step 0 — Identify file type and input scope
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```python
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import os
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input_path = "/mnt/data/..." # from user
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# Detect single file vs directory (multi-file scenario)
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if os.path.isdir(input_path):
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all_files = [
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os.path.join(input_path, f)
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for f in os.listdir(input_path)
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if f.lower().endswith(('.docx', '.doc', '.pdf', '.pptx', '.ppt'))
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]
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print(f"Found {len(all_files)} documents: {all_files}")
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else:
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all_files = [input_path]
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# Route by extension
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ext = os.path.splitext(all_files[0])[-1].lower()
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print(f"File type: {ext}")
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```
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> **Critical rule**: When `input_path` is a directory OR the user says "这些文件" / "所有文档",
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> process **every file** and aggregate. Never stop at the first file.
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---
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### Step 1 — Load sub-skill by format
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| Extension | Sub-skill to load |
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|-----------|------------------|
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| `.docx` / `.doc` | `capability/word-analysis/SUBSKILL.md` |
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| `.pdf` | `capability/pdf-analysis/SUBSKILL.md` |
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| `.pptx` / `.ppt` | `capability/ppt-analysis/SUBSKILL.md` |
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```
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read_file(path="<skills_root>/sn-da-non-spreadsheet-analysis/capability/<format>-analysis/SUBSKILL.md")
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```
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Load **only the sub-skill you need** — do not load all three at once.
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---
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### Step 2 — Parse and extract
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Follow the sub-skill's extraction pattern. For all formats:
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- **Full scan**: iterate all pages/slides/paragraphs — never stop early
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- **Table extraction**: get every table, not just the first one
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- **Image/chart detection**: if a page/slide yields no text, treat it as image-based and call `caption.py`
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---
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### Step 3 — Answer with verification
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After extracting data, verify before answering:
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```python
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# For count/statistics questions: spot-check 3-5 items
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sample = result_list[:3]
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print(f"Sample check: {sample}")
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print(f"Total count: {len(result_list)}")
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# For numeric calculations: print intermediate values
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print(f"Max={max_val}, Min={min_val}, Range={max_val - min_val}")
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# For unit-sensitive answers: always include the unit
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print(f"Answer: {value} {unit}") # e.g., "475 千港元" not just "475"
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```
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---
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## Universal Rules
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### MUST DO
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- **Always iterate all pages/slides/paragraphs** — `for page in doc`, `for slide in prs.slides`, `for para in doc.paragraphs`
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- **When input is a directory**: collect and process all matching files, then aggregate results
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- **For scanned PDFs**: detect empty text → call `caption.py` for OCR
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- **For image-only slides**: text extraction returns empty → render slide as PNG → call `caption.py`
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- **For calculations**: show intermediate values; confirm unit matches the question
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### NEVER DO
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- Do NOT use `pytesseract` or `easyocr` as primary OCR — they are not installed; use `caption.py`
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- Do NOT use PIL pixel analysis to infer chart values — use vision model caption instead
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- Do NOT stop at the first file, first page, or first table
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- Do NOT guess content from filenames — always parse the actual file
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- Do NOT output percentage when the question asks for absolute value (and vice versa)
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---
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## Caption Script (for image/chart content in any document)
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When a page, slide, or embedded image needs vision understanding, load the
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`sn-da-image-caption` skill first, then use its `scripts/caption.py`:
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```
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read_file(path="<skills_root>/sn-da-image-caption/SKILL.md")
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```
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```python
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import subprocess, json
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CAPTION = "/path/to/skills/sn-da-image-caption/scripts/caption.py"
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def caption_image(image_path, prompt=None):
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cmd = ["python3", CAPTION, image_path, "--json"]
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if prompt:
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cmd += ["--prompt", prompt]
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=60)
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if result.returncode != 0:
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raise RuntimeError(f"caption failed: {result.stderr[:200]}")
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return json.loads(result.stdout)["description"]
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# Example prompts by content type:
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# 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。"
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# 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。"
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# Diagram: "描述所有节点和连接关系。"
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```
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---
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## Available sub-skills
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```
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sn-da-non-spreadsheet-analysis/capability/word-analysis/SUBSKILL.md — .docx/.doc
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sn-da-non-spreadsheet-analysis/capability/pdf-analysis/SUBSKILL.md — .pdf
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sn-da-non-spreadsheet-analysis/capability/ppt-analysis/SUBSKILL.md — .pptx/.ppt
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```
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