Analyze Claude Code usage trends over time using the Agent Monitor's analytics API — daily session counts, daily event counts, token volumes by type, model distribution, tool usage rankings, and agent/event type distributions across 365-day retention windows.
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Analyze usage patterns and trends from the Agent Monitor analytics data.
The user provides: $ARGUMENTS
Options: "last 7 days", "last 30 days", "last quarter", "peak hours", "tool trends", "model usage".
| Endpoint | Returns |
|---|---|
GET /api/analytics | Comprehensive analytics object (see schema below) |
GET /api/stats | { total_sessions, active_sessions, active_agents, total_agents, total_events, events_today, ws_connections, agents_by_status, sessions_by_status } |
GET /api/sessions?limit=200 | Full session records with timestamps and metadata |
GET /api/analytics){
"overview": { "total_sessions", "active_sessions", "active_agents", "total_agents", "total_events" },
"tokens": {
"total_input": N, "total_output": N,
"total_cache_read": N, "total_cache_write": N
},
"tool_usage": [{ "tool_name": "...", "count": N }], // top 20
"daily_events": [{ "date": "YYYY-MM-DD", "count": N }], // 365 days
"daily_sessions": [{ "date": "YYYY-MM-DD", "count": N }], // 365 days
"agent_types": [{ "subagent_type": "task"|"explore"|null, "count": N }],
"event_types": [{ "event_type": "PreToolUse"|"PostToolUse"|..., "count": N }],
"avg_events_per_session": N,
"total_subagents": N,
"sessions_by_status": { "active": N, "completed": N, "error": N, "abandoned": N },
"agents_by_status": { "working": N, "completed": N, "error": N, ... }
}
Plot daily_sessions and daily_events for the requested period. Compute:
From analytics tokens (baselines are pre-summed into totals at the DB level):
total_input, total_output, total_cache_read, total_cache_writetotal_cache_read / (total_cache_read + total_input) — trending up = improvingtotal_output / total_input ratio — high = Claude is verboseFrom tool_usage (top 20 tools by event count):
From agent_types + per-session model field:
From sessions_by_status:
completed / total × 100error / total × 100abandoned / total × 100From event_types:
Markdown with tables and ASCII trend indicators (▲▼→). Include period comparison when applicable.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。