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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.

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--- 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