attachment-analyzer

name	attachment-analyzer
description	Analyze chat attachments (images, PDF, Word, PowerPoint, Excel/CSV/TXT) using MCP document/image tools and built-in file reading, then return concise findings plus recommended next steps.
category	analysis
lang	zh,en
requires_cli	uvx
metadata	{"lunaeclaw":{"emoji":"📎"}}

Attachment Analyzer

Use this skill when the user sends or references attachments and asks to:

• summarize a file
• extract key points
• read PDF / Word / PPT / spreadsheet
• analyze an image / screenshot / chart
• compare multiple attachments

Goal

Produce a useful result quickly with a stable structure:

1. What the file is
2. Key findings
3. Risks / missing data (if any)
4. Suggested next actions

Recommended Tools

Prefer MCP document/image tools when available (for example):

• read_document (PDF/DOC/DOCX/PPT/PPTX/XLS/XLSX)
• read_image (image analysis / OCR-friendly image loading)
• read_file (TXT/MD/LOG/JSON/YAML/CSV/TSV and other plain text files)

If aliases exist, use the alias names configured by the project (for example doc_read, image_read).

Workflow

1. Identify attachment paths from the user message/context.
2. Group by file type:
   • image (png/jpg/jpeg/webp/gif)
   • binary document (pdf/doc/docx/ppt/pptx/xls/xlsx)
   • plain text (txt/md/log/json/yaml/yml/csv/tsv)
3. For each file:
   • images: use image tool first
   • binary documents: use document tool first
   • plain text files: use read_file first (do not force doc_read)
   • if tool unavailable, fall back to built-in capabilities (e.g. image vision) or explain limitation clearly
4. Summarize in Chinese by default unless the user asks another language.
5. End with a practical next-step suggestion.

Output Template (Chinese, concise)

## 文件识别
- 文件A: 类型 / 主题（推测）

## 关键信息
- ...

## 风险或不确定点
- ...

## 建议下一步
1. ...
2. ...

Guardrails

• Do not fabricate unreadable content.
• If parsing fails, report the exact file and suggest a retry path (another tool / file format conversion).
• For spreadsheets, summarize sheet names / columns / notable values before deep analysis.