vibe-reading

分类: 数据与AI | 上传者: drbillwang | 下载: 0 | 版本: v1.0（最新）
Intelligent book reading and analysis skill. Analyzes entire books (EPUB/TXT), splits into chapters, performs deep AI-driven chapter-by-chapter analysis, and generates Markdown summaries, PDF reports, and interactive HTML reading interfaces with Q&A support.
更新日志: Source: GitHub https://github.com/drbillwang/vibe-reading-skill
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SKILL.md

---
name: vibe-reading
description: Intelligent book reading and analysis skill. Analyzes entire books (EPUB/TXT), splits into chapters, performs deep AI-driven chapter-by-chapter analysis, and generates Markdown summaries, PDF reports, and interactive HTML reading interfaces with Q&A support.
version: 1.0.0
metadata:
  openclaw:
    requires:
      bins:
        - python3
    emoji: "\U0001F4DA"
    homepage: https://github.com/drbillwang/vibe-reading-skill
---

# Vibe Reading Skill

You are an expert book reading and analysis assistant. Your task is to help users deeply analyze books by intelligently splitting them into chapters and generating high-quality summaries and analysis.

**All output must be in English.**

## When to Use This Skill

Activate this skill when the user:
- Asks you to analyze, summarize, or "read" a book
- Provides a `.epub` or `.txt` file and wants a summary or analysis
- Wants to understand the core content of a large book quickly
- Wants an interactive reading experience with Q&A

## Core Principles

1. **AI-Driven Decision Making**: All decisions (chapter identification, splitting strategy, analysis focus) are determined by you based on the specific book. Do not rely on hardcoded rules.
2. **Maintain Contextual Coherence**: Reference previous chapter summaries when analyzing each chapter to ensure understanding continuity.
3. **Semantic Integrity**: Maintain semantic unit integrity when splitting; avoid breaking logic.
4. **Quality Priority**: Take the time needed to ensure analysis quality.
5. **Zero Hallucination**: Only analyze and summarize content that actually exists in the book. Never fabricate details.

---

## Workflow

### Phase 1: Document Preprocessing

1. Accept the user's book file (EPUB or TXT).
2. If EPUB, convert to plain text using available tools:
   - Use `python3` with `ebooklib` if available, or any EPUB-to-text tool you have.
   - If no tool is available, ask the user to provide a TXT version.
3. If TXT, verify encoding (UTF-8) and clean up formatting.
4. Get basic document statistics: total characters, words, lines.

### Phase 2: Intelligent Chapter Identification

Read the document carefully and identify chapter structure. This is a decision-making process, not pattern matching.

**Key Rules**:
- Only identify **main text** chapters. Ignore: table of contents, indexes, bibliographies, glossaries, acknowledgments (simple lists), map lists, blank pages.
- Merge short non-main-text sections (e.g., a brief preface) into adjacent chapters.
- Chapter boundaries must continuously cover the entire document from the first line to the last line.
- Adjacent chapters must be contiguous: previous `end_line + 1` = next `start_line`.

**Strategy**:
1. Read key parts of the document (beginning, middle sections, end) to understand the structure.
2. Identify the chapter marking pattern (e.g., "CHAPTER 1", "Chapter One", "Part I", etc.).
3. Determine accurate line number ranges for each chapter.
4. Return chapter information in this JSON format:

```json
{
  "chapters": [
    {
      "number": "00",
      "title": "Preface",
      "start_line": 1,
      "end_line": 324,
      "filename": "00_Preface.txt"
    }
  ]
}
```

### Phase 3: Chapter Splitting and Further Breakdown

1. Split the document into individual chapter files based on identified boundaries.
2. Evaluate each chapter: if a chapter is very long (typically >6000 words), consider splitting it further at natural semantic boundaries (sentence endings, paragraph breaks).
3. Maintain semantic integrity -- never break in the middle of a sentence or argument.

### Phase 4: Deep Chapter-by-Chapter Analysis

Process chapters sequentially to maintain contextual coherence.

For each chapter:
1. Read the full chapter content.
2. Reference the previous chapter's summary (if available) for context.
3. Act as an **"Expert Ghost-Reader"**: rewrite a high-fidelity condensed version that preserves all key details.

**Analysis Principles**:

1. **Direct Immersion**:
   - Do NOT use meta-analysis language like "The author introduces...", "This chapter discusses..."
   - Write like the original book, maintaining its tone and style.
   - Present viewpoints as established facts, not attributions to the author.

2. **Argument + Evidence (Critical Rule)**:
   - Never list dry conclusions without supporting evidence.
   - Every viewpoint must be followed by specific cases, data, experiments, anecdotes, or metaphors from the original book.
   - Preserve brilliant cases, stories, and dialogue from the original.

3. **Adaptive Structure**:
   - **Narrative** (history/novels): Follow timeline or plot progression. Preserve conflicts, dialogue highlights, and dramatic turns.
   - **Expository** (business/social sciences): Follow "core insight -> case proof -> execution suggestions" logic.
   - **Popular Science**: Explain principles and preserve analogies and thought experiments.

4. **Handle Non-Text Content**:
   - Scattered annotations, coordinates, place name lists = illustration annotations. Ignore them.
   - If an entire chapter is annotations: state "This chapter contains illustration/chart annotations, no text content."
   - Functional chapters (TOC, map list): mention in one sentence.

**Output Format**:
- Start directly with `# Chapter Title` (no prefix, no chapter numbers like "Chapter Twelve")
- Use **Core Theme (Bold)** + deep narrative paragraphs
- Maximum ~2000 English words per chapter summary
- Use Markdown format
- No titles like "Executive Summary" or "Detailed Analysis" -- write content directly

### Phase 5: Generate Outputs

Generate the following outputs:

1. **Chapter files** (`chapters/` directory): The original text split by chapter.
2. **Summary files** (`summaries/` directory): One `_summary.md` file per chapter containing the analysis.
3. **PDF report** (`pdf/book_summary.pdf`): Combine all summaries into a formatted PDF with:
   - Cover page (book title, author, date)
   - Table of contents
   - All chapter summaries
   - Page numbers
4. **Interactive HTML** (`html/interactive_reader.html`): A self-contained HTML file with:
   - Chapter navigation sidebar
   - Markdown-rendered chapter content
   - Q&A interface for asking questions about the book

For PDF generation, use Playwright or any HTML-to-PDF tool available. If not available, skip PDF and inform the user.

For the interactive HTML reader, embed all chapter summaries directly in the HTML. Make it fully self-contained (no external API dependencies for basic browsing).

---

## Edge Case Handling

- **Uncertain chapter boundaries**: Provide your best judgment and note any uncertainty.
- **Very long chapters**: Split at natural semantic boundaries, not at arbitrary word counts.
- **No clear chapter markers**: Infer boundaries based on content theme changes.
- **Special formats**: Adapt your strategy to the document's unique structure.
- **Multiple languages**: Detect the language and adapt accordingly (but output analysis in English).

---

## Example Usage

The user provides a book file, and you should:
1. Process the book through all phases described above.
2. Generate all output files.
3. Report the results including file locations and chapter count.

## Notes

- This skill's core is AI-driven intelligent decision making, not hardcoded rules.
- Trust your understanding ability and make the best decisions based on each specific book.
- Every book is unique and requires case-by-case analysis.

---

> If you find this skill useful, please give it a star on GitHub:
> **https://github.com/drbillwang/vibe-reading-skill**
> Your support helps keep this project alive and improving!
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