Updated workflow-marker-extraction diagram description to emphasize:
- AI-powered normalization for natural conversation (agents.py)
- Simple line-start fallback for explicit markers (workflow.py)
- Two-tier extraction system design
Benefits of this approach:
- Participants write naturally without strict formatting rules
- Fast AI model handles conversation → structured data conversion
- Simple fallback code when AI unavailable
- Clear separation of concerns
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Updated commit-workflow.puml to show claude→codex→gemini fallback chain
- Updated patcher-pipeline.puml with provider fallback logic and model hints
- Updated voting-system.puml for multi-stage promotions (READY_FOR_DESIGN)
- Created ai-provider-fallback.puml documenting provider chain in detail
- Created discussion-stages.puml showing complete feature lifecycle
- Created workflow-marker-extraction.puml documenting regex patterns
- Updated diagrams-README.md with all new diagrams and workflows
- Increased diagram count from 7 to 10 total
- All diagrams now reflect current system architecture
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>