/usage-trends

name: usage-trends
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

| 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_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.