description: > 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.

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

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_input ratio — 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.