aiwg
Version:
Cognitive architecture for AI-augmented software development with structured memory, ensemble validation, and closed-loop correction. FAIR-aligned artifacts, 84% cost reduction via human-in-the-loop, standards adopted by 100+ organizations.
49 lines (36 loc) • 1.52 kB
Markdown
---
description: Show analytics and metrics from Ralph loop execution history
category: ralph
---
# Ralph Analytics Command
Display aggregate analytics and metrics from Ralph loop execution history.
## Instructions
When invoked, analyze Ralph loop data and present metrics:
1. **Scan Loop History**
- Load all loop records from `.aiwg/ralph/`
- Load reflections from `.aiwg/ralph/reflections/`
- Load debug memory from `.aiwg/ralph/debug-memory/`
2. **Calculate Metrics**
- **Success rate**: % of loops that completed successfully
- **Average iterations**: Mean iterations to completion
- **Reflection reuse rate**: % of reflections applied in subsequent loops
- **Stuck loop rate**: % of loops that hit stuck detection
- **Escalation rate**: % requiring human intervention
3. **Pattern Analysis**
- Most common failure types
- Most effective fix patterns
- Average time per iteration
- Quality trajectory per loop
4. **Display Dashboard**
- Summary metrics table
- Trend indicators (improving/stable/degrading)
- Recommendations for improvement
## Arguments
- `--since [date]` - Analyze loops from date (default: all)
- `--loop [id]` - Analyze specific loop
- `--export [path]` - Export analytics to file
- `--brief` - Show summary only
## References
- @agentic/code/addons/ralph/schemas/reflection-memory.json - Reflection schema
- @agentic/code/addons/ralph/schemas/debug-memory.yaml - Debug memory schema
- @.aiwg/ralph/docs/reflection-memory-guide.md - Guide