translate-book-parallel

aradotso/trending-skills · updated Apr 8, 2026

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$npx skills add https://github.com/aradotso/trending-skills --skill translate-book-parallel
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Skill by ara.so — Daily 2026 Skills collection.

skill.md

Translate Book (Parallel Subagents)

Skill by ara.so — Daily 2026 Skills collection.

A Claude Code skill that translates entire books (PDF/DOCX/EPUB) into any language using parallel subagents. Each chunk gets an isolated context window — preventing truncation and context accumulation that plague single-session translation.

Pipeline Overview

Input (PDF/DOCX/EPUB)
Calibre ebook-convert → HTMLZ → HTML → Markdown
Split into chunks (~6000 chars each)
  │  manifest.json tracks SHA-256 hashes
Parallel subagents (8 concurrent by default)
  │  each: read chunk → translate → write output_chunk*.md
Validate (manifest hash check, 1:1 source↔output match)
Merge → Pandoc → HTML (with TOC) → Calibre → DOCX / EPUB / PDF

Prerequisites

# 1. Calibre (provides ebook-convert)
# macOS
brew install --cask calibre
# Linux
sudo apt-get install calibre
# Or download from https://calibre-ebook.com/

# 2. Pandoc
brew install pandoc        # macOS
sudo apt-get install pandoc # Linux

# 3. Python dependencies
pip install pypandoc beautifulsoup4

Verify all tools are available:

ebook-convert --version
pandoc --version
python3 -c "import pypandoc; print('pypandoc ok')"

Installation

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

Usage in Claude Code

Once the skill is installed, use natural language inside Claude Code:

translate /path/to/book.pdf to Chinese
translate ~/Downloads/mybook.epub to Japanese
/translate-book translate /path/to/book.docx to French

The skill orchestrates the full pipeline automatically.

Supported Languages

Code Language
zh Chinese
en English
ja Japanese
ko Korean
fr French
de German
es Spanish

Language codes are extensible — add new ones in the skill definition.

Running Pipeline Steps Manually

Step 1: Convert to Markdown Chunks

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

This produces inside {book_name}_temp/:

  • chunk0001.md, chunk0002.md, ... (source chunks, ~6000 chars each)
  • manifest.json (SHA-256 hashes for validation)
# For EPUB input
python3 scripts/convert.py /path/to/book.epub --olang ja

# For DOCX input
python3 scripts/convert.py /path/to/book.docx --olang fr

Step 2: Translate (Parallel Subagents)

The skill handles this step — it launches 8 concurrent subagents per batch, each translating one chunk independently:

# Each subagent receives exactly this task:
Read chunk0042.md → translate to target language → write output_chunk0042.md

Resumable: Already-translated chunks (valid output_chunk*.md files) are skipped on re-run.

Step 3: Merge and Build All Formats

python3 scripts/merge_and_build.py \
  --temp-dir book_name_temp \
  --title "《Book Title in Target Language》"

Before merging, validation checks:

  • Every source chunk has a matching output file (1:1)
  • Source chunk hashes match manifest.json (no stale outputs)
  • No output files are empty

Outputs produced:

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

Project Structure

translate-book/
├── SKILL.md                    # Claude Code skill definition (orchestrator)
├── scripts/
│   ├── convert.py              # PDF/DOCX/EPUB → Markdown chunks via Calibre HTMLZ
│   ├── manifest.py             # SHA-256 chunk tracking and merge validation
│   ├── merge_and_build.py      # Merge chunks → HTML → DOCX/EPUB/PDF
│   ├── calibre_html_publish.py # Calibre wrapper for format conversion
│   ├── template.html           # Web HTML template with floating TOC
│   └── template_ebook.html     # Ebook HTML template
└── README.md

How Manifest Validation Works

# scripts/manifest.py (conceptual usage)

# During convert.py — records source hashes
manifest = {
    "chunk0001.md": "sha256:abc123...",
    "chunk0002.md": "sha256:def456...",
    # ...
}

# During merge_and_build.py — validates before merging
# 1. Check every chunk has a corresponding output_chunk
# 2. Re-hash source chunks and compare against manifest
# 3. Reject if any hash mismatches (stale/corrupt output)
# 4. Reject if any output file is empty

If validation fails, the script auto-deletes stale output.md and re-merges from valid chunk outputs.

Real-World Example: Translate a Technical Book

# 1. Install the skill
npx skills add deusyu/translate-book -a claude-code -g

# 2. Open Claude Code in your working directory
cd ~/books

# 3. Say in Claude Code:
# "translate clean-code.pdf to Chinese"

# Claude Code will:
# - Run convert.py to split into chunks
# - Launch 8 parallel subagents per batch
# - Each subagent translates one chunk
# - Validate all outputs via manifest
# - Merge and build all formats

# 4. Outputs appear in:
ls clean-code_temp/
# chunk0001.md  chunk0002.md  ...  (source)
# output_chunk0001.md  ...         (translated)
# manifest.json
# output.md
# book.html
# book.docx
# book.epub
# book.pdf

Resuming an Interrupted Translation

# If translation is interrupted, just re-run the same command:
# "translate clean-code.pdf to Chinese"

# The skill detects existing output_chunk*.md files
# and skips already-translated chunks automatically.
# Only missing or failed chunks are retried.

Changing Output Metadata After Translation

If you need to update the title, author, template, or image assets without re-translating:

# Delete only the final artifacts (keeps translated chunks)
cd book_name_temp/
rm -f output.md book*.html book.docx book.epub book.pdf

# Re-run merge step
python3 ../scripts/merge_and_build.py \
  --temp-dir . \
  --title "《New Title》"

Do NOT delete chunk files — those are your translated content. Only delete final artifacts when changing metadata.

Troubleshooting

Problem Solution
Calibre ebook-convert not found Install Calibre; ensure ebook-convert is in $PATH
Manifest validation failed Source chunks changed — re-run convert.py
Missing source chunk Source file deleted — re-run convert.py to regenerate
Incomplete translation Re-run the skill — resumes from last valid chunk
Changed title/template but output unchanged Delete output.md, book*.html, book.docx, book.epub, book.pdf then re-run merge_and_build.py
output.md exists but manifest invalid Script auto-deletes stale output and re-merges
PDF generation fails Verify Calibre has PDF output support; try ebook-convert --help
Empty output chunks Retry failed chunks; check API rate limits

Diagnosing Chunk Issues

# Check which chunks are missing translation
ls book_temp/chunk*.md | wc -l          # total source chunks
ls book_temp/output_chunk*.md | wc -l   # translated chunks so far

# Find missing output chunks
for f in book_temp/chunk*.md; do
  base=$(basename "$f" .md)
  out="book_temp/output_${base}.md"
  if [ ! -f "$out" ] || [ ! -s "$out" ]; then
    echo "Missing: $out"
  fi
done

# Check manifest
cat book_temp/manifest.json | python3 -m json.tool | head -30

Configuration Tips

  • Chunk size: ~6000 chars per chunk is the default. Smaller chunks = more parallelism but more API calls.
  • Concurrency: Default is 8 parallel subagents per batch. Adjust in SKILL.md if hitting rate limits.
  • Languages: Add new language codes to the skill triggers and translation prompt in SKILL.md.
  • Templates: Customize scripts/template.html and scripts/template_ebook.html for different HTML/ebook styling.

Key Design Principles

  1. Isolated context per chunk — each subagent starts fresh, preventing context overflow on long books
  2. Hash-based integrity — SHA-256 tracking catches stale or corrupt translated chunks before merging
  3. Resumable at chunk granularity — never re-translate what's already done
  4. Format-agnostic input — Calibre handles PDF/DOCX/EPUB normalization before the pipeline begins
  5. Multiple output formats — single pipeline produces HTML, DOCX, EPUB, and PDF simultaneously
how to use translate-book-parallel

How to use translate-book-parallel on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add translate-book-parallel
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aradotso/trending-skills --skill translate-book-parallel

The skills CLI fetches translate-book-parallel from GitHub repository aradotso/trending-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/translate-book-parallel

Reload or restart Cursor to activate translate-book-parallel. Access the skill through slash commands (e.g., /translate-book-parallel) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.666 reviews
  • Hana Wang· Dec 28, 2024

    translate-book-parallel is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Anika Robinson· Dec 16, 2024

    Solid pick for teams standardizing on skills: translate-book-parallel is focused, and the summary matches what you get after install.

  • Tariq Dixit· Dec 16, 2024

    Solid pick for teams standardizing on skills: translate-book-parallel is focused, and the summary matches what you get after install.

  • Shikha Mishra· Dec 8, 2024

    Keeps context tight: translate-book-parallel is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ren Taylor· Dec 8, 2024

    I recommend translate-book-parallel for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chinedu Brown· Dec 4, 2024

    translate-book-parallel has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Nov 27, 2024

    Registry listing for translate-book-parallel matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Charlotte Choi· Nov 23, 2024

    translate-book-parallel fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Hana Okafor· Nov 19, 2024

    translate-book-parallel reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Henry White· Nov 7, 2024

    We added translate-book-parallel from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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