Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill usage-trendsgit clone --depth 1 https://github.com/hoangsonww/Claude-Code-Agent-MonitorWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/usage-trends)<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/usage-trends"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/usage-trends/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hoangsonww/claude-code-agent-monitor/usage-trends"><img src="https://agentmods.dev/badge/skills/hoangsonww/claude-code-agent-monitor/usage-trends.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.00883 |
| Opus 5 | $0.00026 | $0.00441 |
| Sonnet 5 | $0.00010 | $0.00177 |
| Haiku 4.5 | $0.00005 | $0.00088 |
Grade A, and why
usage-trends scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_inputratio — high = Claude is verbose
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 89 lines · 51 tokens per session scan A e9bc152a8736
usage-trends is a skill published in the GitHub repository hoangsonww/Claude-Code-Agent-Monitor (994 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 883 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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