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 "<file_path>" --olang "<target_lang>"

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.

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:

   {
     "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:

   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 field after a partial run, that's fine for this commit — affected chunks won't auto-re-translate yet (commit 3 adds precise re-translation).

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)
• Any custom instructions

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

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.

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):

{
  "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 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： ->

   • 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 "
     '	闭合 attr='...'	目标语言合适的弯引号（如中文 ‘ ’）或 '
     <	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"喝我"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}

markdown文件正文:

4.5. Merge Sub-Agent Meta Into Glossary (after each batch)

Each sub-agent emitted an output_chunk<NNNN>.meta.json alongside its translated chunk. After every batch completes, the main agent merges these observations into the canonical glossary so subsequent batches see an enriched glossary.

1. Run prepare-merge:

   python3 {baseDir}/scripts/merge_meta.py prepare-merge "<temp_dir>"
   

   Capture stdout JSON. It contains four arrays:

   • auto_apply — new entities with no glossary collision and unanimous (target, category) across all proposing chunks.
   • decisions_needed — items requiring main-agent judgment. Each has id, kind, an options array, and the data needed to pick. Kinds:
     • alias — {variant, candidate_source, evidence}. Choices: yes_alias / no_separate_entity / skip.
     • conflict — {entity_source, field, current, proposed, evidence}. Choices: keep_current / accept_proposed / record_in_notes.
     • new_entity_existing_alias — sub-agents propose proposed_source as a new entity, but it's already someone's alias. {proposed_source, currently_alias_of, promoted_variants: [{target_proposal, category, evidence, evidence_chunks}, ...]}. Choices: one use_variant_N per distinct (target, category) promotion variant (promote proposed_source to standalone with that target+category, removing it from the host's aliases) / keep_as_alias / skip.
     • existing_entity_conflict — sub-agents proposed a (target, category) for entity_source that differs from the canonical. Multiple distinct differing proposals all get exposed. {entity_source, current_target, current_category, proposed_variants: [{target_proposal, category, evidence, evidence_chunks}, ...]}. Choices: keep_current / one use_variant_N per competing proposal (overwrites both target AND category, stamps the prior values into notes) / record_in_notes (canonical unchanged; every proposed variant gets logged to notes).
     • alias_or_new_entity — variant has multiple competing options that can't all coexist under v2's surface-form uniqueness rule. Triggered when (a) variant was proposed both as a new standalone entity AND as an alias of one or more candidates, OR (b) variant was proposed as an alias of two or more different candidates with no standalone competitor. {variant, alias_candidates: [{candidate_source, evidence, evidence_chunks}, ...], standalone_variants: [{target_proposal, category, evidence, evidence_chunks}, ...]}. Choices: one use_alias_N per candidate (attach as alias of that candidate), one use_standalone_N per competing standalone proposal (add as standalone with that target+category), or skip.
     • conflicting_new_entity_proposals — {source, variants: [{target_proposal, category, evidence, evidence_chunks}, ...]}. Choices: use_variant_0, use_variant_1, ..., skip.
   • consumed_chunk_ids — every meta file scanned this round (regardless of whether it produced a finding). These hashes get recorded in applied_meta_hashes on apply.
   • malformed_meta_chunk_ids — meta files that failed validation. Quarantined: not consumed, not crashing the run. Surface them in your batch progress.

2. If consumed_chunk_ids is empty → nothing was scanned; skip to Step 5.

3. If consumed_chunk_ids is non-empty but both auto_apply and decisions_needed are empty → still pipe {"auto_apply": [], "decisions": [], "consumed_chunk_ids": [...]} into apply-merge so the hashes get recorded. Skipping this is the bug — no-op metas would re-scan forever otherwise.

4. Otherwise, resolve each decision:

   • Read its evidence quotes inline.

   • Pick one option from its options array.

   • Build a decisions entry that round-trips the original decision plus your choice. The entry MUST include the original kind and (for conflicting_new_entity_proposals) the variants array, so apply-merge can validate and act:

     {"id": "d1", "kind": "alias", "variant": "Taig", "candidate_source": "Tai", "choice": "yes_alias"}
     

5. Pipe the decisions JSON into apply-merge:

   echo '{"auto_apply": [...], "decisions": [...], "consumed_chunk_ids": [...]}' \
     | python3 {baseDir}/scripts/merge_meta.py apply-merge "<temp_dir>"
   

   Surface the summary JSON (auto_applied, decisions_resolved, consumed_chunks, errors) in your batch progress message.

   apply-merge is transactional. If any decision is malformed (wrong choice for kind, missing fields, references a non-existent entity), the entire batch aborts with a non-zero exit and stderr details — no glossary mutation, no hashes recorded. On non-zero exit, fix the offending decision and re-pipe; prepare-merge will surface the same proposals because nothing was consumed.

   Decision order in the input list is not significant. apply-merge internally dispatches entity-creating decisions before alias-attaching ones, so yes_alias decisions whose candidate is created by another decision in the same batch (a use_standalone_N, use_variant_N, or promote_to_separate_entity) succeed regardless of the order you pass them in. Alias chains (e.g. Taighi → Taig where Taig → Tai is also a pending alias decision) resolve via a fixed-point loop within the alias-attacher pass — you don't need to topo-sort or sequence chained aliases manually.

On a fresh run after a previous interrupted batch, prepare-merge will pick up any meta files left behind. Don't manually delete them.

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)

Then run the meta-merge observability snapshot:

python3 {baseDir}/scripts/merge_meta.py status "<temp_dir>"

Surface a one-line summary in the verification report:

Translated chunks: 50 • Meta files: 48 found / 47 consumed • Malformed: 1 (chunk0099 — see stderr) • Chunks missing meta: chunk0017, chunk0042

Severity rules (none of these fail the run — meta is non-blocking):

• unmerged_meta_files > 0 after Step 4.5 ran → bug, flag prominently. Resume should have caught this.
• malformed_meta_files > 0 → sub-agent emitted invalid meta; print chunk_ids and a "fix the file by hand and re-run if you want this chunk's feedback merged" note.
• meta_files_found < translated_chunks → sub-agent-compliance issue (some chunks didn't emit meta at all). Print missing chunk_ids.

Report any chunks that failed translation 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 "<temp_dir>" --title "<translated_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