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PythonReactSEOPDF
GitHub Skill

book-notes

Transcribe handwritten book notes from reMarkable PDFs or paper photos. Trigger phrase "process book notes". Handles single-source book notes with optional chapter structure. Converts handwriting to markdown, applies formatting conventions, generates metadata, creates AI summaries, and appends quotes to a central quotes file. Notes may be in Czech or English.

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安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

查看来源 ↗使用指南

技能说明

Book Notes Processor

Transcribe handwritten book notes into structured Obsidian notes.

Trigger

User says "process book notes" and provides either:

  • A PDF file (reMarkable export)
  • Multiple image files (photos of paper notes)

Language

Notes may be in Czech or English. Preserve the original language throughout. Do not translate.

Workflow

1. Convert and Transcribe

For PDF input:

from pdf2image import convert_from_path
images = convert_from_path('notes.pdf', dpi=150)
for i, img in enumerate(images):
    img.save(f'page_{i+1}.png', 'PNG')

For image input: Accept images directly and process in order.

Then visually read and transcribe each page.

2. Verify Content Type

Book notes typically have:

  • Single book source
  • Optional chapter/section structure (H2 headings)
  • Cohesive narrative around one text

If user provides multi-source research notes, suggest: "This looks like multi-source research notes. Should I use 'process deep dive notes' instead?"

3. Apply Formatting Rules

See ../shared/formatting-rules.md for complete conventions. Key rules:

  • ! at bullet start → italics
  • !!! at bullet start → bold
  • Underscored words → bold
  • Numbered sequences → numbered lists
  • H1 (#) marks book title
  • H2 (##) marks chapters/sections if present
  • Quotes: Author: *"Quote text"*
4. Collect Metadata

Ask user for:

  • Book title (if not clear from notes)
  • Author

Then query Obsidian for existing tags:

obsidian-mcp-tools:search_vault_simple with relevant keywords

Select 3-5 tags that match the vault's existing taxonomy.

5. Generate Summary

If transcribed content exceeds ~1 A4 of text, generate a 1-3 paragraph summary covering:

  • Core argument or thesis
  • Key concepts or frameworks
  • Personal insights or applications

Place summary after frontmatter, before raw transcription.

6. Build Frontmatter
---
title: [Book title]
author: [Author name]
date_created: [YYYY-MM-DD]
source_type: book
tags:
  - [3-5 relevant tags from vault]
---
7. Handle Quotes

If transcription contains quotes (format: Author: "Quote text"):

In transcription:

- Author: *"Quote text"*

Append to Obsidian quotes file 10 - 🧠 Knowledge/3 - 📚 Resources/Learning/Quotes.md:

- Author: "Quote text"
8. Save to Obsidian

Create file in inbox: 02 - 📩 Inbox/[Book Title].md

Use obsidian-mcp-tools:create_vault_file with the complete markdown content.

9. Add Related Notes

Query Obsidian for related existing notes and add a ## Related notes section with wiki-links at the end.

Output Structure

---
[frontmatter]
---

## Summary

[AI-generated summary if content is long enough]

---

# [Book Title]

- [transcribed bullets with formatting]
- *important point*
- **very important point**
- Author: *"Quote text"*

## [Chapter/Section Name] (if applicable)

- [chapter-specific notes]

## Related notes

- [[Related Note 1]]
- [[Related Note 2]]

使用示例

查看来源仓库中的使用示例与配置说明。

查看来源 ↗

来源历史

收录时间
2026-01-20
索引更新时间
2026-01-26
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

GitHub ↗

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