Created comprehensive guide and enhanced sync script for integrating Claude Code
into automated workflows:
1. CLAUDE_INTEGRATION.md:
- 6 different integration options (CLI, file request, git hooks, GitHub Actions, API)
- Detailed examples for each approach
- Pros/cons and use case recommendations
- Best practices and troubleshooting
2. sync_server_file_enhanced.sh:
- Enhanced version of sync_server_file.sh
- Automatic MATLAB file change detection
- Intelligent module mapping (MATLAB → Python)
- Auto-generates formatted request for Claude
- Colored output with progress steps
- Clipboard integration (xclip)
- Editor auto-open option
Features:
✅ Detects which Python modules need updating
✅ Creates markdown request with diff preview
✅ Shows affected files and modules
✅ Copies request to clipboard automatically
✅ Provides step-by-step instructions
✅ Commits MATLAB changes with metadata
Workflow:
1. Run: ./sync_server_file_enhanced.sh
2. Script syncs MATLAB files from server
3. Auto-detects changes and creates request file
4. Open Claude Code and paste/provide the request
5. Claude updates Python code automatically
6. Validate with validation system
Typical usage:
./sync_server_file_enhanced.sh
# → Generates CLAUDE_SYNC_REQUEST_YYYYMMDD_HHMMSS.md
# → Copy to clipboard or open in editor
# → Provide to Claude Code for automatic Python sync
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Created two documentation files to facilitate keeping Python code synchronized
with MATLAB source updates:
1. MATLAB_SYNC_GUIDE.md (comprehensive guide):
- Complete MATLAB ↔ Python file mapping table
- Detailed workflow for applying MATLAB updates
- Request templates and best practices
- Examples for different update scenarios
- Validation procedures
2. sync_matlab_changes.md (quick reference):
- Quick mapping reference
- Minimal request template
- Fast validation commands
- TL;DR for urgent updates
Key Features:
✅ Clear mapping for all 30+ MATLAB files to Python modules
✅ Step-by-step update workflow
✅ Integrated validation with validation system
✅ Git workflow with tagging
✅ Examples for bug fixes, features, new sensors
✅ Time estimates for different update types
Usage:
When MATLAB sources change, provide list of modified files and brief
description. The guide enables rapid analysis and application of changes
to Python codebase with automated validation.
Typical turnaround: 15-60 minutes for standard updates.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
