Rainman Translate Book

May 23, 2026 · View on GitHub

English | 中文

Claude Code skill 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 a Claude Code Skill, 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.

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

Claude Code CLI — installed and authenticated
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

Option A: npx (recommended)

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

Option B: ClawHub

clawhub install translate-book

Option C: Git clone

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

2. Translate a book

In Claude Code, say:

translate /path/to/book.pdf to Chinese

Or 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"

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.

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:

Sample 5 chunks (first, last, 3 evenly-spaced middle).
Extract proper nouns and recurring domain terms; pick canonical translations.
Write <temp_dir>/glossary.json (hand-editable schema below).
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).
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 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:

Reads one source chunk (e.g. chunk0042.md)
Translates to the target language
Uses a per-chunk term table and short read-only previous/next excerpts
Writes the result to output_chunk0042.md
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

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`	Claude Code 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
`scripts/template.html`	Web HTML template with floating TOC
`scripts/template_ebook.html`	Ebook HTML template
`tests/baselines/`	Checked-in baseline book inputs for full-pipeline testing
`tests/.artifacts/`	Ignored full-pipeline test outputs

Troubleshooting

Problem	Solution
`Calibre ebook-convert not found`	Install Calibre and ensure `ebook-convert` is in PATH
`Manifest validation failed`	Source chunks changed since splitting — re-run `convert.py`
`Missing source chunk`	Source file deleted — re-run `convert.py` to regenerate
Incomplete translation	Re-run the skill — it resumes from where it stopped
Changed title/template/assets but output didn't update	Delete existing `output.md`, `book*.html`, `book.docx`, `book.epub`, `book.pdf` from the temp dir, then re-run `merge_and_build.py`
Want page-number footers stripped from PDF output	By default, monotonic page-number sequences (e.g. `1, 2, 3, ...`) are auto-detected and dropped while outliers like years (`1984`), chapter numbers, and citation indices stay preserved. If detection misses your case, pass `--strip-page-numbers` to `convert.py` to aggressively delete every standalone-digit line. The flag aborts if a cached `input.md` or `chunk*.md` already exists — delete them first so the flag actually takes effect.
`output.md exists but manifest invalid`	Stale output — the script auto-deletes and re-merges
`Glossary upgrade rejected: duplicate source`	v2 disallows two terms sharing a source/alias surface form. Edit `glossary.json` to disambiguate (e.g., rename one source from `Apple` to `Apple (Inc.)`) and reload.
PDF generation fails	Ensure Calibre is installed with PDF output support

Roadmap

Tracking issue #7 — name/term inconsistency and pronoun/gender errors across chunks. The pipeline now covers high-frequency entities, alias/spelling drift, adjacent-chunk pronoun context, and selective re-translation after glossary changes. Full-book organic validation remains a future quality pass. The plan is four independently shippable phases.

Design principles

Scripts do bookkeeping; LLMs do semantic merge. State, schemas, dedup, hashing, IO are deterministic Python. Naming, gender attribution, alias judgment, conflict resolution are LLM calls.
Single writer for shared state. Only the main agent writes glossary.json and run_state.json; sub-agents write per-chunk meta files. No locking needed.
Conservative merge. New entities require evidence; alias merges need LLM judgment, not just string similarity; gender starts at unknown and only moves up under explicit evidence; canonical values aren't silently overwritten on conflict.
Three-layer state, three separate files. glossary.json (canonical, sub-agents read), output_chunkNNNN.meta.json (raw per-chunk observations), run_state.json (orchestration).

Phase 1 — Sub-agent feedback + glossary merge (shipped)

Closes the read+write loop. Glossary v2 adds id, aliases, gender, confidence, evidence_refs, notes (v1 files auto-upgrade on first load; the term table is now 3-col and aliases participate in selection). Sub-agents emit output_chunkNNNN.meta.json alongside each translated chunk. scripts/merge_meta.py (prepare-merge / apply-merge / status) merges per-batch with conservative rules: surface-form uniqueness enforced, malformed metas quarantined (warn + skip + count), confidence escalation via both evidence_chunks and used_term_sources, FIFO-cap at 5. See SKILL.md Step 4 / Step 4.5 / Step 5.

Phase 2 — Neighbor context for pronouns (shipped)

scripts/chunk_context.py injects prev_excerpt (last ~300 chars of previous chunk) and next_excerpt (first ~300 chars of next chunk) into each sub-agent prompt as read-only context. No new state files are introduced.

Phase 3 — Selective re-translation (shipped)

Phase 1's batch feedback only improves forward. Selective rerun closes the backward loop with scripts/run_state.py and run_state.json: per-chunk tracking of glossary_version_used, entity_ids_used, output_hash, source hash, and selected entity hashes; five planning rules cover missing/empty output, manifest source drift, untracked outputs, source drift since record, and glossary term selection/hash changes.

Phase 4 — Bootstrap warm-up (experimental, gated on Phase 1 data)

Phase 1 grows the glossary batch-by-batch, so the first batch sees the smallest glossary and has the highest drift risk. Possible approaches: sequential bootstrap, variable concurrency, or skip entirely. Decision belongs to whoever has run the system on real books.

Phase 4 remains gated on real-book evidence. The shipped schemas can still evolve under compatibility-aware migrations if production runs expose gaps.

Parallel track — Pipeline / UX backlog (partly shipped, separate from issue #7)

Recent PR discussions also surfaced several useful workflow improvements, but these are broader than one-off patches and touch repo contracts (artifact names, temp-dir behavior, cleanup semantics, or EPUB compatibility scope). Current status:

Explicit EPUB cover support (shipped). merge_and_build.py --cover <image> passes the image through the HTML -> EPUB Calibre step. --cover-from <epub> / EPUB cover auto-extraction remains out of scope until the project is ready to own EPUB parsing compatibility across different package layouts. (context: closed #3)
Configurable temp workspace location (shipped). convert.py --temp-root <dir> keeps the default cwd-local {book_name}_temp/ behavior unless explicitly overridden. (context: closed #4)
Safer Calibre/Pandoc artifact cleanup (partly shipped). Page-number and Calibre-marker cleanup is regression-tested, preserving years, chapter numbers, and non-monotonic standalone numbers. Continue improving cleanup incrementally under tests. (context: closed #5)
Optional user-facing export names (shipped). merge_and_build.py --export-name <stem> creates alias copies while preserving canonical pipeline artifacts as book.html, book_doc.html, book.docx, book.epub, and book.pdf. (context: closed #6)

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License

MIT