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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Calculate a productivity scorecard from the Agent Monitor's real data.
The user provides: $ARGUMENTS
Options: "today", "this week", "last 30 days", a session ID, or "compare" for period comparison.
| 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 |
From sessions_by_status:
completed / (completed + error + abandoned) × 100From analytics tokens (baselines are pre-summed into totals):
total_cache_read / (total_cache_read + total_input) × 100
total_output / total_input — 0.3–0.8 is balancedFrom event_types:
PostToolUse / Count PreToolUse — should be ~1.0; gap = tool failuresAPIError / total events — should be near 0effectiveness data: subagent completion rates, task success per typeFrom session metadata:
turn_count across sessionstotal_turn_duration_ms / turn_count — lower = fasteravg_events_per_session in analytics overviewthinking_blocks — more thinking = more thorough (neutral metric)From pricing:
total_cost / completed_sessionsWeighted sum → letter grade:
═══════════════════════════════════════
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.
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Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.
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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
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Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
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Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
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Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
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