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Agent Skill · 待分类

book-converter(书籍转换器)

book-converter 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

book-converter 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证信息还不明确,暂无第三方扫描结果,按安装量在待分类类的 11,209 个 Skill 里排第 2,554。

总安装 1,518周安装 62GitHub Stars 8下载 491核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:book-converter(书籍转换器)
来源仓库:https://github.com/bmad-labs/skills
仓库路径:skills/book-converter
安装命令:
npx skills add https://github.com/bmad-labs/skills --skill book-converter
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/bmad-labs/skills --skill book-converter

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

book-converter 暂无可用技能描述,请查看原始 GitHub 仓库获取详细信息。该技能可能涉及电子书格式转换或内容重构,但缺少具体使用说明。建议结合宿主环境(如 Codex、Claude)进一步测试其实际能力边界。安装前应评估是否需要额外依赖或 API 密钥,并确认文件系统访问权限。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/book-converter

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/bmad-labs/skills --skill book-converter;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

book-converter 怎么安装?

在终端执行 npx skills add https://github.com/bmad-labs/skills --skill book-converter。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

book-converter 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

book-converter 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

book-converter 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

book-converter 用的人多吗?

公开数据是累计安装 1,518 次,近一周安装 62 次,下载 491 次,GitHub 8 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

book-converter 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
book-converter

书籍转换器 · 本页

1,5188权限未标注未明确未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Book Converter Skill

Convert EPUB books into professionally formatted Markdown books with AI-assisted quality improvements.

Overview

This skill converts EPUB files into high-quality Markdown documents by:

  1. Using pandoc to extract raw Markdown from EPUB
  2. Creating a structured project directory
  3. Planning and executing AI-driven formatting fixes
  4. Producing chapter-by-chapter formatted output
  5. Generating merged book file with Table of Contents

Quick Start

User provides an EPUB file path:

/Users/username/Downloads/Book.Name.2024.epub

Execute the conversion workflow:

python3 scripts/convert_book.py "/path/to/book.epub"

This initiates the complete conversion process.

Workflow

CRITICAL: Use subagents for all formatting work to avoid polluting main context.

Phase 1: Setup and Extraction (Main Agent)

Run the conversion script:

python3 scripts/convert_book.py "/path/to/book.epub"

This script:

  1. Verifies EPUB file exists
  2. Creates project structure:

- books/book-name/ - Main directory - books/book-name/raw/ - Pandoc output - books/book-name/chapters/ - Formatted chapters - books/book-name/images/ - Extracted images

  1. Runs pandoc to extract Markdown
  2. Copies formatting standards to project directory

Output: Raw Markdown in books/book-name/raw/book-parsed.md

Phase 2: Analysis and Planning (Script + Subagent)

Step 1: Run the structure analysis script (Main Agent):

python3 books/book-name/analyze_structure.py books/book-name

This script:

  • Extracts all headers with line numbers
  • Detects formatting issues by sampling
  • Suggests chapter boundaries
  • Creates STRUCTURE_ANALYSIS.md report (~5-10 KB instead of 35k+ lines)

Step 2: Launch a general subagent to create mapping files:

Task(
  subagent_type="general",
  description="Create chapter map and formatting plan",
  prompt="""Create CHAPTER_MAP.md and FORMATTING_PLAN.md:

1. Read books/book-name/STRUCTURE_ANALYSIS.md (concise report with headers and issues)
2. Read books/book-name/references/chapter-map-template.md for format
3. Read books/book-name/references/formatting-plan-template.md for format
4. Create books/book-name/CHAPTER_MAP.md:
   - Use suggested chapter boundaries from analysis
   - Verify line ranges make sense
   - Create proper slugged filenames
5. Create books/book-name/FORMATTING_PLAN.md:
   - Document issues found in analysis
   - Add severity and priority
   - Note book-specific patterns
6. Update books/book-name/progress.md to mark Phase 2 complete

Return: Summary of chapters found and major issues identified."""
)

Output: CHAPTER_MAP.md, FORMATTING_PLAN.md, and updated progress.md

Phase 3: Chapter Formatting (Use Subagents)

For EACH chapter, launch a separate general subagent:

# Example for Chapter 1
Task(
  subagent_type="general",
  description="Format Chapter 1",
  prompt="""Format Chapter 1 following the chapter formatting workflow.

**Critical Instructions:**
1. Read and follow ALL steps in books/book-name/references/chapter-workflow.md
2. Apply formatting rules from books/book-name/references/formatting-standards.md
3. Use books/book-name/CHAPTER_MAP.md to find line ranges for Chapter 1
4. Read books/book-name/FORMATTING_PLAN.md for known issues to watch for

**Workflow Summary (see chapter-workflow.md for complete details):**

Step 1: Read Standards and Chapter Map
- Read references/formatting-standards.md
- Read CHAPTER_MAP.md for your chapter's line ranges
- Read FORMATTING_PLAN.md for known issues

Step 2: Extract Chapter Content
- Extract Chapter 1 from raw/book-parsed.md using line ranges

Step 3: Identify Issues follow the standards
- Headers using bold instead of #
- Shattered code blocks
- Split paragraphs
- Missing code language identifiers
- Emphasis artifacts [word]
- Corrupted footnotes
- Missing image alt text
- Broken links

Step 4: Apply Formatting Fixes
- Follow the three-pass approach in chapter-workflow.md:
  * First pass: Structure (headers, code blocks)
  * Second pass: Content (paragraphs, emphasis)
  * Third pass: Details (footnotes, images, links)

Step 5: Create Output File
- Write to books/book-name/chapters/chapter-01-title.md
- Use structure from chapter-workflow.md

Step 6: Update Progress
- Update books/book-name/progress.md with completion status
- Document fixes applied

**Quality Checklist (from chapter-workflow.md):**
- [ ] All headers use proper # syntax
- [ ] All code blocks have language identifiers
- [ ] No shattered code blocks remain
- [ ] Text flows naturally without mid-sentence breaks
- [ ] All footnotes have [^N] format with definitions
- [ ] Images have descriptive alt text

Return: Confirmation with summary of fixes applied."""
)

Important:

  • Launch subagents in parallel batches (3-5 at a time) for efficiency
  • Each subagent must read chapter-workflow.md and formatting-standards.md
  • Follow the systematic workflow to ensure consistent quality

Output: Formatted chapters in books/book-name/chapters/

Phase 4: Book Assembly (Main Agent)

The merge_book.py script is already copied to your project directory. Simply run it:

python3 books/book-name/merge_book.py books/book-name

The script will:

  1. Read CHAPTER_MAP.md for chapter order
  2. Load all formatted chapters from chapters/
  3. Extract headers for Table of Contents
  4. Fix image paths (relative to final location)
  5. Combine all chapters in order
  6. Generate comprehensive TOC
  7. Output to books/book-name-book.md

Output: books/book-name-book.md with complete formatted book

Note: The merge script is reusable - no need to create it per book!

Critical: Chapter Formatting Requirements

Every subagent in Phase 3 MUST:

  1. Read chapter-workflow.md first - Contains the complete step-by-step process
  2. Read formatting-standards.md - Contains all formatting rules (678 lines)
  3. Follow the workflow systematically - Don't skip steps
  4. Use the three-pass approach:

- First pass: Fix structure (headers, code blocks) - Second pass: Fix content (paragraphs, emphasis) - Third pass: Fix details (footnotes, images, links)

  1. Complete the quality checklist - Verify all items before finishing

Why this matters:

  • Ensures consistent quality across all chapters
  • Prevents common mistakes (skipped issues, inconsistent style)
  • Proven process from Clean Code Collection (35k+ lines)
  • Each chapter is only formatted once - must be thorough

The workflow documents are your complete instructions - trust them!

Subagent Usage Principles

Never process book content in main context. Always use subagents to:

  1. Keep main context clean: Book content is large and pollutes context
  2. Enable parallelization: Format multiple chapters simultaneously
  3. Isolate formatting work: Each chapter gets fresh context
  4. Avoid token limits: Raw content can exceed context windows

Subagent Selection: Always use subagent_type="general" for all book processing tasks.

Progress Tracking

Create and maintain books/book-name/progress.md:

# Book Name - Conversion Progress

## Phase 1: Setup ✓
- [x] EPUB extracted
- [x] Project structure created

## Phase 2: Planning ✓
- [x] Chapter map created (15 chapters identified)
- [x] Formatting plan documented

## Phase 3: Chapter Formatting (5/15 complete)
- [x] Front Matter
- [x] Chapter 1: Introduction
- [x] Chapter 2: Getting Started
- [x] Chapter 3: Advanced Topics
- [x] Chapter 4: Best Practices
- [ ] Chapter 5: Performance
- [ ] ...

## Phase 4: Assembly
- [ ] Merge script created
- [ ] Final book generated

Update after each subagent completes.

Quality Standards

All formatted output must meet these criteria:

  • Headers: Use proper # syntax, not bold text
  • Code Blocks: Include language identifiers, merge shattered blocks
  • Text Flow: Join split sentences into natural paragraphs
  • Emphasis: Use *italic* and **bold**, not [brackets]
  • Footnotes: Standard [^1] format with definitions
  • Images: Descriptive alt text, not generic filenames
  • Links: Clean anchors, no PDF conversion artifacts

Complete standards reference: references/formatting-standards.md

Example Usage

User Request:

"Convert this EPUB to Markdown: /Users/john/Downloads/Effective.Java.3rd.Edition.epub"

Skill Execution:

  1. Run conversion script to extract content
  2. Analyze structure and create chapter map
  3. Format each chapter using AI subagents
  4. Merge into final book with TOC
  5. Provide user with books/effective-java-final.md

Scripts

  • convert_book.py: Main conversion script (Phase 1) - Extracts EPUB and sets up project
  • analyze_structure.py: Structure analyzer (Phase 2) - Extracts headers and detects issues efficiently
  • merge_book.py: Reusable merge script (Phase 4) - Combines all chapters into final book

References

  • formatting-standards.md: Complete formatting rules (loaded as needed during formatting)
  • chapter-workflow.md: Detailed chapter formatting workflow (loaded as needed)
  • progress-template.md: Template for progress tracking file
  • chapter-map-template.md: Template for chapter mapping
  • formatting-plan-template.md: Template for formatting issue documentation

Notes

  • High Quality Focus: Manual AI-driven formatting ensures prose flows naturally
  • No Automated Scripts: Formatting requires human-like judgment for line joining
  • Preserve Content: Never alter meaning or remove content
  • Code Accuracy: Ensure code blocks are syntactically complete

平台分布

Codex

36.04%
按下载量换算177

Claude

26.62%
按下载量换算131

Cursor

17.97%
按下载量换算88

Gemini CLI

9.39%
按下载量换算46

安全审计

暂无安全审计结果可展示。

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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