hoangsonww Usage Trends
Analyze usage patterns and trends from the Agent Monitor analytics data.
Input
The user provides: $ARGUMENTS
Options: "last 7 days", "last 30 days", "last quarter", "peak hours", "tool trends", "model usage".
Data Sources
| 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 |
Analytics response schema (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, ... }
}
Trend Analyses to Produce
1. Daily Activity Trend
Plot daily_sessions and daily_events for the requested period. Compute:
- Average sessions/day and events/day
- Week-over-week delta (%)
- Peak day and quietest day
2. Token Volume Trends
From analytics tokens (baselines are pre-summed into totals at the DB level):
- Total tokens:
total_input,total_output,total_cache_read,total_cache_write - Cache efficiency over time:
total_cache_read / (total_cache_read + total_input)— trending up = improving - Output intensity:
total_output / total_inputratio — high = Claude is verbose
3. Tool Usage Ranking
From tool_usage (top 20 tools by event count):
- Bar chart data (tool name → count)
- Tool diversity: unique tools used
- Subagent spawns: count of "Agent" tool uses (each = a subagent launched)
4. Model Distribution
From agent_types + per-session model field:
- Which models are used most frequently
- Subagent type distribution: main (null) vs task vs explore vs code-review
5. Session Health Distribution
From sessions_by_status:
- Completion rate:
completed / total × 100 - Error rate:
error / total × 100 - Abandoned rate:
abandoned / total × 100
6. Event Type Distribution
From event_types:
- PreToolUse/PostToolUse ratio (should be ~1:1; gap = tools failing)
- Compaction frequency relative to session count
- APIError count (quota hits, rate limits, overloaded)
Output
Markdown with tables and ASCII trend indicators (▲▼→). Include period comparison when applicable.