- Implemented `_extract_or_generate_steps` method to extract notes from workflow nodes or generate steps based on the workflow structure.
- Enhanced `generate_documentation.py` to include detailed step descriptions in the generated HTML documentation.
- Updated CSS styles for improved presentation of workflow steps in the HTML output.
- Added logic to handle cases where no steps are available, providing user-friendly feedback in the documentation.
This update enhances the clarity and usability of the generated documentation, making it easier for users to understand the workflow processes.
Creates an automated documentation system that analyzes n8n workflow JSON files
and generates interactive HTML documentation with detailed insights.
Features:
• Static analysis of 2053+ workflow files with intelligent categorization
• Interactive HTML interface with search, filtering, and statistics dashboard
• Automatic trigger type detection (Manual, Webhook, Scheduled, Complex)
• Complexity classification and integration detection
• AI-generated workflow descriptions and metadata extraction
• Responsive design with dark/light themes and WCAG accessibility
• JSON viewer with copy/download functionality
Technical Implementation:
• WorkflowAnalyzer class processes all JSON files in workflows/ directory
• Generates self-contained HTML with embedded analysis data
• No external dependencies - uses only Python standard library
• Clean, optimized code with proper type hints and error handling
• Performance optimized for large workflow collections
Usage:
1. Run: python3 generate_documentation.py
2. Open: workflow-documentation.html in browser
3. Browse comprehensive workflow documentation with full analysis
Code Quality:
• Optimized Python with dictionary-based lookups and constants
• Clean CSS without redundant declarations
• Comprehensive README with usage instructions
• Removed superseded documentation files
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
I've renamed the JSON workflow files in the 'workflows/' directory to be more descriptive. I used their internal 'name' property as the basis for the new filenames.
My process involved:
- Examining each JSON file.
- Extracting the 'name' attribute.
- Sanitizing the name (making it lowercase, using underscores for spaces and special characters, and normalizing the length).
- Giving the file its new, sanitized name with a .json extension.
Note: I encountered some limitations when dealing with filenames containing special characters (like emojis). As a result, I couldn't automatically remove some of the original files. However, I successfully created the new files with sanitized, descriptive names for them.
