| name | productivity-score |
| description | Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.
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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
4. Velocity (20% weight)
From session metadata:
- Turns per session: average
turn_count across sessions
- Turn speed: average
total_turn_duration_ms / turn_count — lower = faster
- Events per session: from
avg_events_per_session in analytics overview
- Thinking depth: average
thinking_blocks — more thinking = more thorough (neutral metric)
5. Cost Efficiency (20% weight)
From pricing:
- Cost per completed session:
total_cost / completed_sessions
- Cost trend: comparing current period to previous (decreasing = improving)
- Model optimization: sessions using expensive models (Opus) for tasks subagents handle with Haiku/Sonnet
Overall Score
Weighted sum → letter grade:
- A+ (95-100), A (90-94), B+ (85-89), B (80-84), C+ (75-79), C (70-74), D (60-69), F (<60)
Output Format
═══════════════════════════════════════
PRODUCTIVITY SCORE: 87/100 (B+)
═══════════════════════════════════════
Completion Rate ████████░░ 80/100
Token Efficiency █████████░ 92/100
Tool Effectiveness████████░░ 85/100
Velocity █████████░ 88/100
Cost Efficiency █████████░ 90/100
═══════════════════════════════════════
Then: top 3 strengths, top 3 improvement areas with actionable steps, and period comparison if available.
