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
- `config.txt` — pipeline configuration with metadata

### 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 eac