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.
1---2name: productivity-score3description: 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.4---56# Productivity Score78Calculate a productivity scorecard from the Agent Monitor's real data.910## Input1112The user provides: **$ARGUMENTS**1314Options: "today", "this week", "last 30 days", a session ID, or "compare" for period comparison.1516## Data Sources1718| Endpoint | Returns |19|----------|---------|20| `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 |21| `GET /api/sessions?limit=100` | Sessions with metadata JSON: `thinking_blocks`, `turn_count`, `total_turn_duration_ms`, `usage_extras` (service_tier, speed, inference_geo) |22| `GET /api/pricing/cost` | Total cost with per-model breakdown |23| `GET /api/workflows/{sessionId}` | 11 workflow datasets: stats, orchestration, toolFlow, effectiveness, patterns, modelDelegation, errorPropagation, concurrency, complexity, compaction, cooccurrence |2425## Score Components (each 0–100)2627### 1. Completion Rate (20% weight)28From `sessions_by_status`:29- `completed / (completed + error + abandoned) × 100`30- Bonus for high completed-to-active ratio31- Penalty for abandoned sessions (wasted work)3233### 2. Token Efficiency (20% weight)34From analytics `tokens` (baselines are pre-summed into totals):35- **Cache hit rate**: `total_cache_read / (total_cache_read + total_input) × 100`36 - Above 60% = excellent, below 30% = poor37- **Output concentration**: `total_output / total_input` — 0.3–0.8 is balanced3839### 3. Tool Effectiveness (20% weight)40From `event_types`:41- **Success ratio**: Count `PostToolUse` / Count `PreToolUse` — should be ~1.0; gap = tool failures42- **API error rate**: Count `APIError` / total events — should be near 043- From workflow `effectiveness` data: subagent completion rates, task success per type4445### 4. Velocity (20% weight)46From session metadata:47- **Turns per session**: average `turn_count` across sessions48- **Turn speed**: average `total_turn_duration_ms / turn_count` — lower = faster49- **Events per session**: from `avg_events_per_session` in analytics overview50- **Thinking depth**: average `thinking_blocks` — more thinking = more thorough (neutral metric)5152### 5. Cost Efficiency (20% weight)53From pricing:54- **Cost per completed session**: `total_cost / completed_sessions`55- **Cost trend**: comparing current period to previous (decreasing = improving)56- **Model optimization**: sessions using expensive models (Opus) for tasks subagents handle with Haiku/Sonnet5758## Overall Score5960Weighted sum → letter grade:61- **A+** (95-100), **A** (90-94), **B+** (85-89), **B** (80-84), **C+** (75-79), **C** (70-74), **D** (60-69), **F** (<60)6263## Output Format6465```66═══════════════════════════════════════67 PRODUCTIVITY SCORE: 87/100 (B+)68═══════════════════════════════════════69 Completion Rate ████████░░ 80/10070 Token Efficiency █████████░ 92/10071 Tool Effectiveness████████░░ 85/10072 Velocity █████████░ 88/10073 Cost Efficiency █████████░ 90/10074═══════════════════════════════════════75```7677Then: top 3 strengths, top 3 improvement areas with actionable steps, and period comparison if available.
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