/productivity-score

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.

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.