Epic: Implement Workflow and LLM Configuration

[Haydart]

Epic: Trend Detection System

Overview

Implement a standalone real-time trend detection workflow that monitors Solana memecoin launches via WebSocket, classifies tokens using dual-stage LLM processing (cheap classification + expensive creation), and maintains Firebase collections with transaction-based race condition prevention. The system leverages existing domain architecture patterns (Event → Processing → Execution) to minimize complexity while delivering production-ready trend categorization.

Tasks Created

• Implement Trend Firebase Executor #169 - Implement Trend Firebase Executor
• Implement Trend Classification Stage #170 - Implement Trend Classification Stage
• Implement Workflow and LLM Configuration #171 - Implement Workflow and LLM Configuration
• Implement Data Models and Action Classes #172 - Implement Data Models and Action Classes
• Implement Unit Tests and LangFuse Integration #173 - Implement Unit Tests and LangFuse Integration
• Implement New Trend Creation Stage #174 - Implement New Trend Creation Stage
• Implement Action Stage and Processor #175 - Implement Action Stage and Processor
• Extend Firebase Service with Trend Operations #176 - Extend Firebase Service with Trend Operations
• Implement TrendDetectionSource with Firestore Watch #177 - Implement TrendDetectionSource with Firestore Watch
• Implement Metadata Extraction Stage #178 - Implement Metadata Extraction Stage

Total tasks: 10 ✅
Progress: 100%

Status

🎉 COMPLETED - All 10 tasks successfully implemented and tested.

Next Steps

Run /pm:epic-close trends to formally close this epic.

[Haydart]

✅ Epic completed - all 10 tasks done

Implemented System:

• Real-time trend detection workflow with WebSocket monitoring
• Dual-stage LLM processing (classification + creation)
• Firebase storage with transaction-based deduplication
• Web UI with trend tracking and detection toggle controls
• Ultra-granular trend classification (90% confidence threshold)

Key Features:

• Event → Processing → Execution architecture
• Reactive Firebase watchers (no polling)
• In-memory cache with incremental updates
• Comprehensive logging and error handling
• Toast notifications and auto-refresh UI

Duration: 21 hours (from 2025-10-03T00:03:32Z to 2025-10-03T21:12:07Z)