translate-book-parallel▌
aradotso/trending-skills · updated Apr 8, 2026
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Skill by ara.so — Daily 2026 Skills collection.
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.mdif hitting rate limits. - Languages: Add new language codes to the skill triggers and translation prompt in
SKILL.md. - Templates: Customize
scripts/template.htmlandscripts/template_ebook.htmlfor different HTML/ebook styling.
Key Design Principles
- Isolated context per chunk — each subagent starts fresh, preventing context overflow on long books
- Hash-based integrity — SHA-256 tracking catches stale or corrupt translated chunks before merging
- Resumable at chunk granularity — never re-translate what's already done
- Format-agnostic input — Calibre handles PDF/DOCX/EPUB normalization before the pipeline begins
- Multiple output formats — single pipeline produces HTML, DOCX, EPUB, and PDF simultaneously
How to use translate-book-parallel on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches translate-book-parallel from GitHub repository aradotso/trending-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
Submit your Claude Code skill and start earning
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★66 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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