productivity-score

# Productivity Score Calculate a productivity scorecard from the Agent Monitor's real data. ## Input The user provides: **$ARGUMENTS** Options: "today", "this week", "last 30 days", a session ID, or "compare" for period comparison. ## Data Sources | Endpoint | Returns | |----------|---------| | `GET /api/analytics` | Token totals (`total_input`, `total_output`, `total_cache_read`, `total_cache_write` — baselines pre-summed), tool_usage top 20, daily_events/sessions, event_types, sessions_by_status, agents_by_status, avg_events_per_session, total_subagents | | `GET /api/sessions?limit=100` | Sessions with metadata JSON: `thinking_blocks`, `turn_count`, `total_turn_duration_ms`, `usage_extras` (service_tier, speed, inference_geo) | | `GET /api/pricing/cost` | Total cost with per-model breakdown | | `GET /api/workflows/{sessionId}` | 11 workflow datasets: stats, orchestration, toolFlow, effectiveness, patterns, modelDelegation, errorPropagation, concurrency, complexity, compaction, cooccurrence | ## Score Components (each 0–100) ### 1. Completion Rate (20% weight) From `sessions_by_status`: - `completed / (completed + error + abandoned) × 100` - Bonus for high completed-to-active ratio - Penalty for abandoned sessions (wasted work) ### 2. Token Efficiency (20% weight) From analytics `tokens` (baselines are pre-summed into totals): - **Cache hit rate**: `total_cache_read / (total_cache_read + total_input) × 100` - Above 60% = excellent, below 30% = poor - **Output conc...