nntrivi2001
17d56d0e01
Clean break, no compatibility alias. Renames the product name, the CLI binary (`ccam` -> `agenthub`), the env var prefix (`CCAM_` -> `AGENTHUB_`), the Prometheus metric namespace (`ccam_*` -> `agenthub_*`), the tmux session prefix (`ccam-lane-<id>` -> `agenthub-lane-<id>`), the per-repo runtime profile directory (`.ccam/profile` -> `.agenthub/profile`), the lanes state root (`~/.claude/ccam-lanes` -> `~/.claude/agenthub-lanes`), the bundled plugins (`plugins/ccam*` -> `plugins/agenthub*`) and their slash commands (`/ccam-doctor` -> `/agenthub-doctor`, `/ccam:*` -> `/agenthub:*`). Because there is no alias, an existing install has to be updated by hand: rename its `.ccam/profile` directory and any `CCAM_*` variables, and reinstall the plugin under its new name. Two other name families are deliberately left alone here, since they are not just strings: the `Claude-Code-Monitor` repository/marketplace name, and the `agent-dashboard` npm package plus its `~/.claude/agent-dashboard` runtime directory, which holds the live database and needs a migration rather than a rename. Verified: server 1406/1406, client 459/459, `grep -i ccam` clean across tracked files, `openapi.yaml` regenerated (95 paths, unchanged count).
88 lines
3.3 KiB
Markdown
88 lines
3.3 KiB
Markdown
---
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description: >
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Analyze Claude Code usage trends over time using the Agent Monitor's
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analytics API — daily session counts, daily event counts, token volumes
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by type, model distribution, tool usage rankings, and agent/event type
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distributions across 365-day retention windows.
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---
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# Usage Trends
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Analyze usage patterns and trends from the Agent Monitor analytics data.
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## Input
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The user provides: **$ARGUMENTS**
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Options: "last 7 days", "last 30 days", "last quarter", "peak hours", "tool trends", "model usage".
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## Data Sources
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| Endpoint | Returns |
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|----------|---------|
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| `GET /api/analytics` | Comprehensive analytics object (see schema below) |
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| `GET /api/stats` | `{ total_sessions, active_sessions, active_agents, total_agents, total_events, events_today, ws_connections, agents_by_status, sessions_by_status }` |
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| `GET /api/sessions?limit=200` | Full session records with timestamps and metadata |
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### Analytics response schema (`GET /api/analytics`)
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```json
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{
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"overview": { "total_sessions", "active_sessions", "active_agents", "total_agents", "total_events" },
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"tokens": {
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"total_input": N, "total_output": N,
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"total_cache_read": N, "total_cache_write": N
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},
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"tool_usage": [{ "tool_name": "...", "count": N }], // top 20
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"daily_events": [{ "date": "YYYY-MM-DD", "count": N }], // 365 days
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"daily_sessions": [{ "date": "YYYY-MM-DD", "count": N }], // 365 days
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"agent_types": [{ "subagent_type": "task"|"explore"|null, "count": N }],
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"event_types": [{ "event_type": "PreToolUse"|"PostToolUse"|..., "count": N }],
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"avg_events_per_session": N,
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"total_subagents": N,
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"sessions_by_status": { "active": N, "completed": N, "error": N, "abandoned": N },
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"agents_by_status": { "working": N, "completed": N, "error": N, ... }
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}
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```
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## Trend Analyses to Produce
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### 1. Daily Activity Trend
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Plot `daily_sessions` and `daily_events` for the requested period. Compute:
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- **Average sessions/day** and **events/day**
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- Week-over-week delta (%)
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- Peak day and quietest day
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### 2. Token Volume Trends
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From analytics tokens (baselines are pre-summed into totals at the DB level):
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- Total tokens: `total_input`, `total_output`, `total_cache_read`, `total_cache_write`
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- **Cache efficiency over time**: `total_cache_read / (total_cache_read + total_input)` — trending up = improving
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- **Output intensity**: `total_output / total_input` ratio — high = Claude is verbose
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### 3. Tool Usage Ranking
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From `tool_usage` (top 20 tools by event count):
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- Bar chart data (tool name → count)
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- Tool diversity: unique tools used
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- Subagent spawns: count of "Agent" tool uses (each = a subagent launched)
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### 4. Model Distribution
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From `agent_types` + per-session model field:
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- Which models are used most frequently
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- Subagent type distribution: main (null) vs task vs explore vs code-review
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### 5. Session Health Distribution
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From `sessions_by_status`:
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- Completion rate: `completed / total × 100`
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- Error rate: `error / total × 100`
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- Abandoned rate: `abandoned / total × 100`
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### 6. Event Type Distribution
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From `event_types`:
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- PreToolUse/PostToolUse ratio (should be ~1:1; gap = tools failing)
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- Compaction frequency relative to session count
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- APIError count (quota hits, rate limits, overloaded)
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## Output
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Markdown with tables and ASCII trend indicators (▲▼→). Include period comparison when applicable.
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