Claude-Code-Agent-Monitor

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.