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 concentration: total_output / total_input, 0.3, 0.8 is balanced

3. Tool Effectiveness (20% weight)

From event_types:

• Success ratio: Count PostToolUse / Count PreToolUse, should be ~1.0; gap = tool failures
• API error rate: Count APIError / total events, should be near 0
• From workflow effectiveness data: subagent completion rates, task success per type