translate-book

Rainman Translate Book

English | 中文

An agent skill for Codex, Claude Code, and OpenClaw that translates entire books (PDF/DOCX/EPUB) into any language using parallel subagents.

Inspired by claude_translater. The original project uses shell scripts as its entry point, coordinating the Claude CLI with multiple step scripts to perform chunked translation. This project restructures the workflow as an agent skill for Codex, Claude Code, and OpenClaw, using subagents to translate chunks in parallel, with manifest-driven integrity checks, resumable runs, and multi-format output unified into a single pipeline. As the project structure and implementation differ significantly from the original, this is an independent project rather than a fork.

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How It Works

Input (PDF/DOCX/EPUB)
  │
  ▼
Calibre ebook-convert → HTMLZ → HTML → Markdown
  │
  ▼
Split into chunks (chunk0001.md, chunk0002.md, ...)
  │  manifest.json tracks chunk hashes
  ▼
Parallel subagents (8 concurrent by default)
  │  each subagent: read 1 chunk → translate → write output_chunk*.md
  │  batched to respect API rate limits
  ▼
Validate (manifest hash check, 1:1 source↔output match)
  │
  ▼
Merge → Pandoc → HTML (with TOC) → Calibre → DOCX / EPUB / PDF

Each chunk gets its own independent subagent with a fresh context window. This prevents context accumulation and output truncation that happen when translating a full book in a single session.

Features

• Parallel subagents — 8 concurrent translators per batch, each with isolated context
• Resumable + selective re-translation — chunk-level resume, with run_state.json tracking glossary-sensitive re-translation
• Neighbor context — each chunk can see short read-only excerpts from adjacent chunks for pronoun and entity resolution
• Manifest validation — SHA-256 hash tracking prevents stale or corrupt outputs from being merged
• Multi-format output — HTML (with floating TOC), DOCX, EPUB, PDF
• Optional output controls — explicit EPUB cover, custom temp root, and user-facing export aliases
• Multi-language — zh, en, ja, ko, fr, de, es (extensible)
• PDF/DOCX/EPUB input — Calibre handles the conversion heavy lifting

Prerequisites

• Agent runtime — Codex, Claude Code, or OpenClaw, installed and ready to run skills
• Calibre — ebook-convert command must be available (download)
• Pandoc — for HTML↔Markdown conversion (download)
• Python 3 with:
  • pypandoc — required (pip install pypandoc)
  • beautifulsoup4 — optional, for better TOC generation (pip install beautifulsoup4)

Quick Start

1. Install the skill

Codex

npx skills add deusyu/translate-book -a codex -g

Or install it manually:

mkdir -p ~/.agents/skills
git clone https://github.com/deusyu/translate-book.git ~/.agents/skills/translate-book

Restart Codex if the newly installed skill does not appear.

Claude Code

npx skills add deusyu/translate-book -a claude-code -g

Or install it manually:

mkdir -p ~/.claude/skills
git clone https://github.com/deusyu/translate-book.git ~/.claude/skills/translate-book

OpenClaw

openclaw skills install @deusyu/translate-book

2. Translate a book

Codex

In the Codex CLI or IDE extension, enter:

$translate-book Translate /path/to/book.pdf into Chinese.

Codex can also select the skill automatically when your request matches its description.

Claude Code and OpenClaw

Ask the agent:

translate /path/to/book.pdf to Chinese

In Claude Code, you can also use the slash command:

/translate-book translate /path/to/book.pdf to Japanese

The skill handles the full pipeline automatically — convert, chunk, translate in parallel, validate, merge, and build all output formats.

3. Find your outputs

All files are in {book_name}_temp/:

File	Description
output.md	Merged translated Markdown
book.html	Web version with floating TOC
book.docx	Word document
book.epub	E-book
book.pdf	Print-ready PDF

Repository Test Assets

• Checked-in baseline inputs live under tests/baselines/<book-id>/.
• Generated full-pipeline outputs live under tests/.artifacts/ and should not be committed.
• Because scripts/convert.py writes {book_name}_temp/ under the current working directory, run repository baseline tests from inside tests/.artifacts/ to keep generated files out of the repo root.

Full-Pipeline Baseline Example

mkdir -p tests/.artifacts
cd tests/.artifacts
python3 ../../scripts/convert.py ../baselines/standard-alice/standard-alice.epub --olang zh
# then run translation via the skill
python3 ../../scripts/merge_and_build.py --temp-dir standard-alice_temp --title "test"

Feedback and Contributions

Please open a detailed GitHub issue instead of starting with a pull request. This project is maintained as an AI-assisted skill pipeline, and changes need to be evaluated against the current orchestration rules, chunk/manifest contracts, baseline assets, and release flow in one maintainer-owned context.

Pull requests are not the preferred contribution path and may be closed in favor of an issue. If you already have a patch, include the idea, key diff, failing case, or verification notes in the issue; the maintainer may rework or split the implementation before merging.

A useful issue should include:

• Current behavior and expected behavior
• Input format and environment, such as PDF/DOCX/EPUB, OS, Python, Calibre, and Pandoc versions
• Minimal reproduction steps or a small public-domain sample when possible
• Logs, screenshots, or generated file names that show the failure

Pipeline Details

Step 1: Convert

python3 scripts/convert.py /path/to/book.pdf --olang zh

Calibre converts the input to HTMLZ, which is extracted and converted to Markdown, then split into chunks (~6000 chars each). A manifest.json records the SHA-256 hash of each source chunk for later validation, and a source_fingerprint.json ties the temp dir to the exact source bytes it was built from — re-running against a replaced source file aborts instead of silently reusing stale chunks. Temp dirs created before fingerprinting are adopted with a warning on first re-run.

By default the working directory is {book_name}_temp/ under the current directory. Use --temp-root /path/to/work to keep the same leaf directory name under a different parent.

Step 1.5: Glossary (term consistency across chunks)

Each chunk is translated by a fresh-context sub-agent, which means the same proper noun can drift across multiple translations on a 100-chunk book. To fix this, the skill builds a glossary before translation:

1. Sample 5 chunks (first, last, 3 evenly-spaced middle).
2. Extract proper nouns and recurring domain terms; pick canonical translations.
3. Write <temp_dir>/glossary.json (hand-editable schema below).
4. Run python3 scripts/glossary.py count-frequencies <temp_dir> to populate per-term frequencies (ASCII terms use word-boundary regex so cat doesn't match category; CJK terms use substring; single-CJK-char terms are rejected; aliases count toward the term they belong to).
5. For each chunk, the orchestrator calls python3 scripts/glossary.py print-terms-for-chunk <temp_dir> chunkNNNN.md and injects the resulting 3-column (原文 | 别名 | 译文) markdown table into that chunk's prompt as a hard constraint. Term selection = (terms whose source OR any alias appears in this chunk) ∪ (top-N most-frequent book-wide).

{
  "version": 2,
  "terms": [
    {"id": "Manhattan", "source": "Manhattan", "target": "曼哈顿",
     "category": "place", "aliases": [], "gender": "unknown",
     "confidence": "medium", "frequency": 12,
     "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 — fix the file by hand and reload.

Edit glossary.json between runs to fix translations; existing glossary.json is never overwritten — delete it to rebuild from scratch. scripts/run_state.py records which glossary terms each chunk used, so later glossary changes (including target, category, and aliases edits) only re-translate affected chunks after the state has been recorded.

Step 2: Translate (parallel subagents)

The skill launches subagents in batches (default: 8 concurrent). Each subagent:

1. Reads one source chunk (e.g. chunk0042.md)
2. Translates to the target language
3. Uses a per-chunk term table and short read-only previous/next excerpts
4. Writes the result to output_chunk0042.md
5. Writes output_chunk0042.meta.json observations for glossary feedback

Before launching subagents, scripts/run_state.py plan <temp_dir> decides which chunks need translation, which existing outputs only need state recording, and which are unchanged. Use --retranslate-untracked only when adopting an old temp dir whose existing outputs should be forced through the current glossary. If a run is interrupted, re-running skips chunks that already have valid output files and current state. Failed chunks are retried once automatically.

Step 3: Merge & Build

python3 scripts/merge_and_build.py --temp-dir book_temp --title "《translated title》"

Optional output flags:

python3 scripts/merge_and_build.py --temp-dir book_temp --title "《translated title》" --cover cover.jpg --export-name "translated-title"

--cover passes an explicit image to the EPUB Calibre step. --export-name creates alias copies such as translated-title.epub while preserving the canonical book.* pipeline artifacts.

Before merging, the script validates:

• Every source chunk has a corresponding output file (1:1 match)
• Source chunk hashes match the manifest (no stale outputs)
• No output files are empty, blank (whitespace-only), or unreadable — a blank chunk aborts the merge instead of silently dropping its content

Then: merge → Pandoc HTML → inject TOC → Calibre generates DOCX, EPUB, PDF.

Note: {book_name}_temp/ is a working directory for a single translation run. If you change the title, author, output language, template, or image assets, either use a fresh temp directory or delete the existing final artifacts (output.md, book*.html, book.docx, book.epub, book.pdf) before re-running.

Project Structure

File	Purpose
SKILL.md	Agent skill definition — orchestrates the full pipeline
scripts/convert.py	PDF/DOCX/EPUB → Markdown chunks via Calibre HTMLZ
scripts/manifest.py	Chunk manifest: SHA-256 tracking and merge validation
scripts/glossary.py	Glossary management: per-chunk term tables for consistent terminology
scripts/chunk_context.py	Read-only previous/next chunk excerpts for sub-agent prompts
scripts/meta.py	Per-chunk sub-agent observation file schema (output_chunkNNNN.meta.json)
scripts/merge_meta.py	Batch-boundary merge: sub-agent observations → canonical glossary
scripts/run_state.py	Selective re-translation planner and run_state.json recorder
scripts/merge_and_build.py	Merge chunks → HTML → DOCX/EPUB/PDF
scripts/calibre_html_publish.py	Calibre wrapper for format conversion