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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Drill into APIError events: how many, when, where, and most likely why.
The user provides: $ARGUMENTS
This may be:
| Endpoint | Returns |
|---|---|
GET /api/analytics | event_types (total APIError count), daily_events (365d) — APIError volume and trend over time |
GET /api/events?session_id=X | Per-session event stream — each APIError carries summary, data, and timestamp used to classify the cause |
GET /api/sessions?limit=N | Sessions with id, model, started_at — attribute each error to a model and place it on the timeline |
From GET /api/analytics: total APIError count and its share of total_events. Use daily_events to chart APIErrors over the requested window and flag any day that spikes above the window mean.
For each session in scope, pull GET /api/events?session_id=X and collect APIError events. Group by session_id and, via GET /api/sessions, by model. Report the top affected sessions and which model accounts for the most errors.
Inspect each error's summary/data and bucket it:
Compaction events).summary.
Report the count and percentage in each bucket.List the most recent APIErrors with timestamp, session_id, model, classified cause, and a one-line summary excerpt.
session_id, model, timestamp, and summary values — never invent a cause not supported by the payload; bucket as "Other" when unclear.curl cannot reach http://localhost:4820, tell the user to start the dashboard with npm start from the repo root.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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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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Report concurrency and parallelism for a session — how many agents ran in parallel, concurrency-lane utilization, peak parallel width, and serialization bottlenecks (sequential chains that could have run as parallel lanes) — using the Agent Monitor workflow intelligence API. Use when checking whether a multi-agent session used parallelism efficiently.
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