Simplified marker extraction architecture:
- AI normalization (agents.py) handles natural conversation
- Simple line-start matching for explicit markers as fallback
- Removed complex regex patterns (DECISION_PATTERN, QUESTION_PATTERN, ACTION_PATTERN)
- Participants can now write naturally without strict formatting rules
This implements the original design intent: fast AI model normalizes conversational
text into structured format, then simple parsing logic extracts it.
Benefits:
- More flexible for participants (no strict formatting required)
- Simpler code (startswith() instead of regex)
- Clear separation: AI for understanding, code for mechanical parsing
- Cost-effective (fast models for simple extraction task)
Updated workflow-marker-extraction.puml to show patterns in notes
instead of inline text (fixes PlantUML syntax error).
🤖 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>
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