Usage trends

Skill hoangsonww/Claude-Code-Agent-Monitor/plugins/ccam-analytics/skills/usage-trends

🚀 A real-time monitoring dashboard for Claude Code, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, and WebSockets. It tracks sessions, agent activity, tool usage, and subagent orchestration, providing live analytics, a Kanban status board, status notifications, a cute buddy, and an interactive web UI/MacOS/Windows native app.

Install

npx -y skills add hoangsonww/Claude-Code-Agent-Monitor --skill usage-trends

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What its author says it does

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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.

SKILL.md

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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.