Translate Book

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python3 {baseDir}/scripts/convert.py "<file_path>" --olang "<target_lang>"

Glob: {filename}_temp/chunk*.md

python3 {baseDir}/scripts/run_state.py plan "<temp_dir>"

python3 {baseDir}/scripts/run_state.py record "<temp_dir>" chunk0001 chunk0002 ...

python3 {baseDir}/scripts/glossary.py print-terms-for-chunk "<temp_dir>" "chunk<NNNN>.md"

python3 {baseDir}/scripts/chunk_context.py "<temp_dir>" "chunk<NNNN>.md"

{
  "schema_version": 1,
  "new_entities": [
    {"source": "Taig", "target_proposal": "泰格", "category": "person",
     "evidence": "<≤200-char quote from the chunk>"}
  ],
  "alias_hypotheses": [
    {"variant": "Taig", "may_be_alias_of_source": "Tai",
     "evidence": "<≤200-char quote>"}
  ],
  "attribute_hypotheses": [
    {"entity_source": "Tai", "attribute": "gender", "value": "male",
     "confidence": "high", "evidence": "<≤200-char quote>"}
  ],
  "used_term_sources": ["Tai", "Manhattan"],
  "conflicts": [
    {"entity_source": "Tai", "field": "target", "injected": "泰",
     "observed_better": "太一", "evidence": "<≤200-char quote>"}
  ]
}

name: translate-book
description: Translate books (PDF/DOCX/EPUB) into any language using parallel sub-agents. Converts input -> Markdown chunks -> translated chunks -> HTML/DOCX/EPUB/PDF.
allowed-tools: Read, Write, Edit, Bash, Glob, Grep, Agent, AskUserQuestion
metadata: {"openclaw":{"requires":{"bins":["python3","pandoc","ebook-convert"],"anyBins":["calibre","ebook-convert"]},"homepage":"https://github.com/deusyu/translate-book"}}

---
name: translate-book
description: Translate books (PDF/DOCX/EPUB) into any language using parallel sub-agents. Converts input -> Markdown chunks -> translated chunks -> HTML/DOCX/EPUB/PDF.
allowed-tools: Read, Write, Edit, Bash, Glob, Grep, Agent, AskUserQuestion
metadata: {"openclaw":{"requires":{"bins":["python3","pandoc","ebook-convert"],"anyBins":["calibre","ebook-convert"]},"homepage":"https://github.com/deusyu/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`)
- **temp_root**: Optional directory under which `{filename}_temp/` should be created
- **epub_cover**: Optional explicit cover image path for EPUB output
- **export_name**: Optional filename stem for user-facing output aliases
- **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:

```bash
python3 {baseDir}/scripts/convert.py "<file_path>" --olang "<target_lang>"
```

If the user provided `temp_root`, add `--temp-root "<temp_root>"`. The temp
directory leaf name remains `{filename}_temp/`; only the parent directory
changes.

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
- `source_fingerprint.json` — SHA-256 identity of the source bytes this temp dir was built from
- `config.txt` — pipeline configuration with metadata

If `convert.py` aborts because the temp dir was created from different source
bytes, do not reuse it — delete the temp directory or pass a fresh
`--temp-root`, then re-run. Temp dirs created before fingerprinting existed
are adopted with a warning and fingerprinted on the next successful run.

### 3. Discover Source Chunks

Use Glob to find all source chunks:

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

Exclude `output_chunk*.md` from the source list. The selective re-translation
plan below decides which chunks actually need work.

### 3.5. Build Glossary (term consistency)

A separate sub-agent translates each chunk with a fresh context. Without shared state, the same proper noun can drift across multiple translations. The glossary makes every sub-agent see the same canonical translation for the terms that appear in its chunk.

If `<temp_dir>/glossary.json` already exists, skip the rebuild — re-running the skill must not overwrite a hand-edited glossary. To force a rebuild, delete the file.

Otherwise:

1. **Sample chunks**: read `chunk0001.md`, the last chunk, and 3 evenly-spaced middle chunks. If `chunk_count < 5`, sample all of them.
2. **Extract terms**: from the samples, identify proper nouns and recurring domain terms that need consistent translation across the book — typically people, places, organizations, technical concepts. Translate each into the target language. Skip generic vocabulary that any translator would render the same way.
3. **Write `glossary.json`** in the temp dir, matching this v2 schema:

   ```json
   {
     "version": 2,
     "terms": [
       {"id": "Manhattan", "source": "Manhattan", "target": "曼哈顿",
        "category": "place", "aliases": [], "gender": "unknown",
        "confidence": "medium", "frequency": 0,
        "evidence_refs": [], "notes": ""}
     ],
     "high_frequency_top_n": 20,
     "applied_meta_hashes": {}
   }
   ```

   Existing v1 `glossary.json` files are auto-upgraded to v2 on first load. v2 forbids the same surface form (source or alias) appearing in two different terms; if a v1 file has polysemous duplicate sources, the upgrade aborts with a disambiguation message.

4. **Count frequencies** by running:

   ```bash
   python3 {baseDir}/scripts/glossary.py count-frequencies "<temp_dir>"
   ```

   This scans every `chunk*.md` (excluding `output_chunk*.md`), updates each term's `frequency` field, and writes back atomically.

The glossary is hand-editable. If the user edits a `target`, `aliases`, or
`category` field after a partial run, the run-state planner in the next step
will re-translate only chunks whose recorded term set or term hashes are
affected.

### 3.7. Plan Selective Re-translation

Run:

```bash
python3 {baseDir}/scripts/run_state.py plan "<temp_dir>"
```

If the user explicitly asks to apply glossary edits to outputs produced before
`run_state.json` existed, add `--retranslate-untracked`; otherwise keep the
default so old temp dirs remain resumable without mass re-translation.

Capture stdout JSON:
- `translation_chunk_ids` — chunks to translate in this run.
- `record_only_chunk_ids` — existing valid outputs that need `run_state.json`
  records but do not need translation.
- `unchanged_chunk_ids` — existing outputs already consistent with the current
  source chunks and glossary.

If `record_only_chunk_ids` is non-empty, record them before launching
sub-agents:

```bash
python3 {baseDir}/scripts/run_state.py record "<temp_dir>" chunk0001 chunk0002 ...
```

Use `translation_chunk_ids` as the work queue for Step 4. If it is empty, 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 `<temp_dir>/chunk<NNNN>.md` to {TARGET_LANGUAGE} and write the result to `<temp_dir>/output_chunk<NNNN>.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)
- A per-chunk term table (see "Term table assembly" below)
- Read-only neighboring chunk excerpts (see "Neighbor context assembly" below)
- Any custom instructions

**Term table assembly** — before spawning a sub-agent, run:

```bash
python3 {baseDir}/scripts/glossary.py print-terms-for-chunk "<temp_dir>" "chunk<NNNN>.md"
```

Capture stdout. The CLI emits a 3-column markdown table (`原文 | 别名 | 译文`) of every term that either appears in this chunk (by source OR any alias) OR is in the top-N most-frequent terms book-wide. Inject the table as `{TERM_TABLE}` in rule #13 of the translation prompt. **If stdout is empty (no glossary, or no relevant terms), omit rule #13 from this chunk's prompt entirely** — do not leave a dangling `{TERM_TABLE}` placeholder.

**Neighbor context assembly** — before spawning a sub-agent, run:

```bash
python3 {baseDir}/scripts/chunk_context.py "<temp_dir>" "chunk<NNNN>.md"
```

Capture stdout. The CLI emits prompt-ready read-only excerpts: the last ~300
characters of the previous chunk and the first ~300 characters of the next
chunk when those files exist. Inject this block as `{NEIGHBOR_CONTEXT}`. If
stdout is empty, omit the neighbor-context block entirely. The sub-agent must
not translate neighboring excerpts or copy them into the output; they are only
for pronoun, gender, and entity-resolution context.

**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`
4. Write observations to `output_chunk0001.meta.json` matching the schema below. **Non-blocking** — leave fields empty if unsure; do not invent entities. Always emit the file (even if all arrays are empty), because its presence + content hash is how the main agent tracks whether feedback was already merged.

**Sub-agent meta schema** (`output_chunk<NNNN>.meta.json`):

```json
{
  "schema_version": 1,
  "new_entities": [
    {"source": "Taig", "target_proposal": "泰格", "category": "person",
     "evidence": "<≤200-char quote from the chunk>"}
  ],
  "alias_hypotheses": [
    {"variant": "Taig", "may_be_alias_of_source": "Tai",
     "evidence": "<≤200-char quote>"}
  ],
  "attribute_hypotheses": [
    {"entity_source": "Tai", "attribute": "gender", "value": "male",
     "confidence": "high", "evidence": "<≤200-char quote>"}
  ],
  "used_term_sources": ["Tai", "Manhattan"],
  "conflicts": [
    {"entity_source": "Tai", "field": "target", "injected": "泰",
     "observed_better": "太一", "evidence": "<≤200-char quote>"}
  ]
}
```

**Do NOT include a `chunk_id` field** — chunk identity is derived from the filename. Putting it in the payload creates a hallucination hole and validation will reject the file.

The meta file is read by the main agent later and merged into `glossary.json` (see `merge_meta.py`). Sub-agents should fill the schema honestly: cite real quotes from the chunk, never invent entities to "look productive". An empty meta is a perfectly valid output.

**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. 删除空链接、不必要的字符和如: 行末的'\\'。页码已由 convert.py 上游处理，不要再删除独立的数字行（可能是年份 1984、章节编号、引用编号等正文内容）。
4. 保证格式和语义准确翻译内容自然流畅
5. 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。
6. 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。
7. 必须保留所有图片引用，包括：
   - 所有 `![alt](path)` 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：`![Figure 1: Data Flow](media/image-001.png)` -> `![图1：数据流](media/image-001.png)`
   - **原始 HTML 标签（如 `<img alt="..." />`、`<a title="...">`）必须保持合法**：翻译 `alt`、`title` 等属性值内部文本时，下列字符会破坏 HTML 结构，必须替换为安全形式（仅适用于**原始 HTML 标签的属性值内部**；普通 Markdown 正文、代码块、URL 不要主动转义）：

     | 字符 | 在属性值内的危险 | 替换为 |
     |------|---------------|--------|
     | `"` | 闭合 `attr="..."` | 目标语言合适的弯引号（如中文 `“` `”`）或 `&quot;` |
     | `'` | 闭合 `attr='...'` | 目标语言合适的弯引号（如中文 `‘` `’`）或 `&#39;` |
     | `<` | 被解析为新标签 | `&lt;` |
     | `>` | 被解析为标签结束 | `&gt;` |
     | `&` | 被解析为实体起始（除非已是 `&xxx;`） | `&amp;` |

     不要修改 `src`、`href` 等结构性属性的值，只翻译可见文本属性（`alt`、`title`）。

     - 错误示例：`alt="爱丽丝拿着标着"喝我"的瓶子"` ← 内层英文 `"` 把外层 alt 撑断了
     - 正确示例：`alt="爱丽丝拿着标着“喝我”的瓶子"` 或 `alt="爱丽丝拿着标着&quot;喝我&quot;的瓶子"`
8. 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
9. 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）
10. 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
11. 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
12. {CUSTOM_INSTRUCTIONS if provided}
13. 术语一致性：以下术语必须严格使用指定译法，不要自行变换。表格中"原文"列**或"别名"列**任一形式出现在正文中时，都必须翻译为"译文"列对应的形式。

{TERM_TABLE}

邻居上下文（只读，不要翻译，不要写入输出，只用于判断代词、性别、别名和跨 chunk 指代；为空则省略）:

{NEIGHBOR_CONTEXT}

markdown文件正文:

---

### 4.5. Merge Sub-Age