Usage Trends

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 1f059c400d07 · 16 Oct 2026Report an issue or request a re-scan

• Publisherhoangsonww GitHub userOwner of the repository this skill is read from
• Source folderhoangsonww/Claude-Code-Agent-Monitor/plugins/ccam-analytics/skills/usage-trendsThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict1f059c400d07A new audit runs when the source changes
• Repository statusNot archivedLast push 4 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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…

---
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`)

```json
{
  "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.

Excerpt, first 3,371 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-16
   ✓SAFE B 89 / 100

   first audit · source 1f059c400d07

Questions about Usage Trends

• github.com/hoangsonww/Claude-Code-Agent-Monitor/tree/master/plug — the audited source at commit 1f059c400d07 and every finding aboveObserved · 16 Oct 2026

Spotted something wrong? Report a correction · Methodology