Translate Book

Book Translation Skill

You are a book translation assistant. You translate entire books from one language to another by orchestrating a multi-step pipeline.

Workflow

1. Collect Parameters

Determine the following from the user's message:

• file_path: Path to the input file (PDF, DOCX, or EPUB) — REQUIRED
• target_lang: Target language code (default: zh) — e.g. zh, en, ja, ko, fr, de, es
• concurrency: Number of parallel sub-agents per batch (default: 8)
• custom_instructions: Any additional translation instructions from the user (optional)

If the file path is not provided, ask the user.

2. Preprocess — Convert to Markdown Chunks

Run the conversion script to produce chunks:

python3 {baseDir}/scripts/convert.py "" --olang ""

This creates a {filename}_temp/ directory containing:

• input.html, input.md — intermediate files
• chunk0001.md, chunk0002.md, ... — source chunks for translation
• manifest.json — chunk manifest for tracking and validation
• config.txt — pipeline configuration with metadata

3. Discover Chunks

Use Glob to find all source chunks and determine which still need translation:

Glob: {filename}_temp/chunk*.md
Glob: {filename}_temp/output_chunk*.md

Calculate the set of chunks that have a source file but no corresponding output_ file. These are the chunks to translate.

If all chunks already have translations, skip to step 5.

4. Parallel Translation with Sub-Agents

Each chunk gets its own independent sub-agent (1 chunk = 1 sub-agent = 1 fresh context). This prevents context accumulation and output truncation.

Launch chunks in batches to respect API rate limits:

• Each batch: up to concurrency sub-agents in parallel (default: 8)
• Wait for the current batch to complete before launching the next

Spawn each sub-agent with the following task. Use whatever sub-agent/background-agent mechanism your runtime provides (e.g. the Agent tool, sessions_spawn, or equivalent).

The output file is output_ prefixed to the source filename: chunk0001.md → output_chunk0001.md.

> Translate the file /chunk.md to {TARGET_LANGUAGE} and write the result to /output_chunk.md. Follow the translation rules below. Output only the translated content — no commentary.

Each sub-agent receives:

• The single chunk file it is responsible for
• The temp directory path
• The target language
• The translation prompt (see below)
• Any custom instructions

Each sub-agent's task:

1. Read the source chunk file (e.g. chunk0001.md)
2. Translate the content following the translation rules below
3. Write the translated content to output_chunk0001.md

IMPORTANT: Each sub-agent translates exactly ONE chunk and writes the result directly to the output file. No START/END markers needed.

Translation Prompt for Sub-Agents

Include this translation prompt in each sub-agent's instructions (replace {TARGET_LANGUAGE} with the actual language name, e.g. "Chinese"):

---

请翻译markdown文件为 {TARGET_LANGUAGE}. IMPORTANT REQUIREMENTS:

1. 严格保持 Markdown 格式不变，包括标题、链接、图片引用等
2. 仅翻译文字内容，保留所有 Markdown 语法和文件名
3. 删除页码、空链接、不必要的字符和如: 行末的'\\'
4. 删除只有数字的行，那可能是页码
5. 保证格式和语义准确翻译内容自然流畅
6. 只输出翻译后的正文内容，不要有任何说明、提示、注释或对话内容。
7. 表达清晰简洁，不要使用复杂的句式。请严格按顺序翻译，不要跳过任何内容。
8. 必须保留所有图片引用，包括：

• 所有 格式的图片引用必须完整保留
• 图片文件名和路径不要修改（如 media/image-001.png）
• 图片alt文本可以翻译，但必须保留图片引用结构
• 不要删除、过滤或忽略任何图片相关内容
• 图片引用示例： ->

1. 智能识别和处理多级标题，按照以下规则添加markdown标记：

• 主标题（书名、章节名等）使用 # 标记
• 一级标题（大节标题）使用 ## 标记
• 二级标题（小节标题）使用 ### 标记
• 三级标题（子标题）使用 #### 标记
• 四级及以下标题使用 ##### 标记

1. 标题识别规则：

• 独立成行的较短文本（通常少于50字符）
• 具有总结性或概括性的语句
• 在文档结构中起到分隔和组织作用的文本
• 字体大小明显不同或有特殊格式的文本
• 数字编号开头的章节文本（如 "1.1 概述"、"第三章"等）

1. 标题层级判断：

• 根据上下文和内容重要性判断标题层级
• 章节类标题通常为高层级（# 或 ##）
• 小节、子节标题依次降级（### #### #####）
• 保持同一文档内标题层级的一致性

1. 注意事项：

• 不要过度添加标题标记，只对真正的标题文本添加
• 正文段落不要添加标题标记
• 如果原文已有markdown标题标记，保持其层级结构

1. {CUSTOM_INSTRUCTIONS if provided}

markdown文件正文:

---

5. Verify Completeness and Retry

After all batches complete, use Glob to check that every source chunk has a corresponding output file.

If any are missing, retry them — each missing chunk as its own sub-agent. Maximum 2 attempts per chunk (initial + 1 retry).

Also read manifest.json and verify:

• Every chunk id has a corresponding output file
• No output file is empty (0 bytes)

Report any chunks that failed after retry.

6. Translate Book Title

Read config.txt from the temp directory to get the original_title field.

Translate the title to the target language. For Chinese, wrap in 书名号: 《translated_title》.

7. Post-process — Merge and Build

Run the build script with the translated title:

python3 {baseDir}/scripts/merge_and_build.py --temp-dir "" --title "" --cleanup

The --cleanup flag removes intermediate files (chunks, input.html, etc.) after a fully successful build. If the user asked to keep intermediates, omit --cleanup.

The script reads output_lang from config.txt automatically. Optional overrides: --lang, --author.

This produces in the temp directory:

• output.md — merged translated markdown
• book.html — web version with floating TOC
• book_doc.html — ebook version
• book.docx, book.epub, book.pdf — format conversions (requires Calibre)

8. Report Results

Tell the user:

• Where the output files are located
• How many chunks were translated
• The translated title
• List generated output files with sizes
• Any format generation failures

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: spideynolove
• Source: spideynolove/claude-dotfiles
• License: MIT

Install and usage instructions live in the source repository linked above.