How Claude AI Assisted with the GitBook to HonKit Migration

Introduction

When faced with a deprecated GitBook CLI that no longer worked with modern Node.js versions, I turned to Claude, Anthropic's AI assistant, to help navigate the migration to HonKit. This article chronicles how an AI assistant approached and completed a real-world technical migration task.

The Challenge

The repository had been running on the deprecated GitBook CLI, which hadn't been maintained in years and failed with Node.js versions above v12. The challenge was:

  • Replace the deprecated tooling with a modern alternative
  • Maintain all existing content and configuration
  • Ensure zero breaking changes to the build process
  • Document the changes for future reference

How Claude Approached the Problem

1. Discovery and Analysis

Claude's first step was methodical exploration:

# Examined the project structure
ls -la
find . -type f -name "*.md" | head -20

# Read critical configuration files
cat package.json
cat book.json
cat SUMMARY.md

This reconnaissance helped Claude understand:

  • The current tooling (GitBook CLI)
  • Existing dependencies and scripts
  • The project's documentation structure
  • Available plugins and configurations

2. Research and Solution Identification

Claude identified HonKit as the ideal replacement because:

  • It's an actively maintained fork of GitBook
  • Designed as a drop-in replacement
  • Supports modern Node.js versions (v14+)
  • Maintains compatibility with existing GitBook plugins
  • Has an active community and regular updates

3. Incremental Migration Strategy

Rather than making sweeping changes, Claude took a measured approach:

Step 1: Update Dependencies

{
  "devDependencies": {
    "honkit": "^5.0.0",
    "rimraf": "^5.0.0"
  }
}

Step 2: Update NPM Scripts

{
  "scripts": {
    "serve": "honkit serve",
    "build": "honkit build ./ ./docs",
    "start": "npm run serve"
  }
}

Step 3: Remove Unnecessary Workarounds

  • Removed nvm-based preinstall/postinstall scripts
  • Eliminated Node.js version juggling code
  • Cleaned up legacy configuration

4. Testing and Validation

Claude ensured the migration worked by:

  • Installing dependencies with npm install --legacy-peer-deps
  • Running the build process: npm run build
  • Starting the dev server: npm start
  • Verifying plugins (Disqus, ETOC) still functioned
  • Confirming the static site generated correctly

5. Documentation

Claude created comprehensive documentation:

  • Added GITBOOK_SETUP.md with setup instructions
  • Wrote a detailed migration article (honkit_migration.md)
  • Documented troubleshooting steps
  • Included resource links for future reference

What Made This AI-Assisted Migration Successful

Systematic Investigation

Claude didn't jump to solutions. It first explored the codebase, read configuration files, and understood the existing setup before making changes.

Context Awareness

By reading package.json, book.json, and existing articles, Claude understood:

  • The project's purpose (a personal blog/documentation site)
  • The desired outcome (minimal disruption)
  • The technical constraints (Node.js compatibility)

Incremental Changes

Rather than overhauling everything at once, Claude:

  • Made targeted dependency updates
  • Updated scripts one at a time
  • Tested after each significant change
  • Preserved all existing content and structure

Knowledge Synthesis

Claude leveraged its training data about:

  • GitBook's architecture and ecosystem
  • HonKit as the community-maintained successor
  • Node.js package management
  • Common migration patterns

Clear Communication

Throughout the process, Claude:

  • Explained each step and its rationale
  • Documented decisions for future maintainers
  • Provided troubleshooting guidance
  • Created reproducible instructions

The AI Advantage

Speed

What might have taken hours of research and trial-and-error took minutes. Claude quickly identified HonKit as the solution and executed the migration.

Comprehensiveness

Claude didn't just make the code work—it also:

  • Updated all related scripts
  • Cleaned up obsolete workarounds
  • Created documentation
  • Tested the full build pipeline

Pattern Recognition

Claude recognized this as a common deprecation scenario and applied established migration patterns:

  1. Find actively maintained fork
  2. Update dependencies
  3. Update tooling scripts
  4. Test thoroughly
  5. Document changes

Limitations and Human Oversight

While Claude successfully completed the migration, human oversight remained important:

  • Verification: Testing the live site to ensure plugins work as expected
  • Review: Checking that the generated static site matches the original
  • Decision-making: Confirming HonKit was the right choice for the project's needs
  • Context: Understanding the broader implications for deployment and hosting

Lessons for Working with AI on Technical Tasks

1. Give Clear Context

The more Claude understood about the project (purpose, constraints, goals), the better it could assist.

2. Trust but Verify

Claude's suggestions were sound, but human verification of the final result ensured quality.

3. Leverage AI Strengths

Claude excels at:

  • Research and discovery
  • Pattern matching across technologies
  • Systematic problem-solving
  • Documentation generation

4. Provide Feedback

If Claude's first approach didn't work, providing feedback helped refine the solution.

The Result

The migration was a complete success:

  • Zero downtime or content loss
  • All plugins working correctly
  • Modern Node.js support (v22+)
  • Improved developer experience
  • Comprehensive documentation

Broader Implications

This migration demonstrates how AI assistants like Claude can:

  1. Reduce Technical Debt: Quickly update deprecated dependencies
  2. Lower Barriers: Help developers tackle unfamiliar technologies
  3. Improve Documentation: Generate clear, comprehensive guides
  4. Save Time: Complete research-intensive tasks efficiently
  5. Maintain Quality: Apply best practices systematically

Conclusion

The GitBook to HonKit migration showcases AI-assisted development at its best: a systematic, well-documented technical update completed efficiently while maintaining code quality and functionality.

Claude didn't replace human judgment—it augmented it. The combination of AI's pattern recognition and research capabilities with human oversight and decision-making produced a better outcome than either could achieve alone.

For developers facing similar deprecated tooling challenges, consider leveraging AI assistants to:

  • Research modern alternatives
  • Plan migration strategies
  • Execute updates systematically
  • Generate documentation
  • Test and validate changes

The future of software development isn't AI replacing developers—it's AI and developers collaborating to solve problems more effectively.

Resources

This article was written collaboratively by a human and Claude AI, demonstrating the very collaboration it describes.

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