Polls the Agent Monitor /api/stats endpoint several times over a short window and reports the live deltas in active_sessions, active_agents, events_today, and ws_connections so you can see activity moving in real time. Use when watching the dashboard for live changes rather than a one-time snapshot.
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Watch the dashboard's live counters change over a short window by polling
/api/stats a few times and reporting the deltas.
The user provides: $ARGUMENTS
Interpreted as the watch shape: number of polls and/or interval (e.g. 5x3s =
5 samples 3 seconds apart). Defaults when empty: 5 samples, ~3 seconds apart
(a ~15-second window). A bare number means that many samples at the default
interval; a bare duration means the default sample count at that interval.
| Endpoint | Returns |
|---|---|
GET /api/stats (polled) | { total_sessions, active_sessions, active_agents, total_agents, total_events, events_today, ws_connections, agents_by_status, sessions_by_status } |
Poll GET /api/stats once per interval for the configured number of samples,
recording the timestamp and the four watched counters each time. Pace the polls
with a short wait between requests; keep the total window short (seconds, not
minutes) so it stays interactive.
If the very first poll fails to connect, the dashboard is down — stop and tell
the user to start it with npm start (or npm run dev) from the repo root,
then retry.
State the sample count, interval, and total elapsed window.
A Markdown table — one row per poll — with columns:
#, time, active_sessions, active_agents, events_today, ws_connections.
For each of the four watched counters, report the net change from the first to
the last sample using ▲ (increase), ▼ (decrease), or = (no change). Note any
mid-window spikes or dips visible in the timeline.
One line: is the dashboard actively receiving traffic (counters moving) or idle (flat) over the window?
events_today: ▲ +7.还没有评价。欢迎分享你的使用感受。
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Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.
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Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
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Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.
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Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
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Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
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