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claude-code

Claude-Code-Agent-Monitor usage-trends

install
source · Clone the upstream repo
git clone https://github.com/hoangsonww/Claude-Code-Agent-Monitor
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/hoangsonww/Claude-Code-Agent-Monitor "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/ccam-analytics/skills/usage-trends" ~/.claude/skills/hoangsonww-claude-code-agent-monitor-usage-trends && rm -rf "$T"
manifest: plugins/ccam-analytics/skills/usage-trends/SKILL.md
source content

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

EndpointReturns
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.