Scan recent Claude Code activity for errors and failure signals across all sessions using Agent Monitor data — APIError events and PreToolUse→PostToolUse gaps (tools that started but never completed) — then group failures by tool and model and rank them by frequency. Use when checking for errors or asking "what's failing right now".
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Sweep recent events across sessions for error and failure signals, then rank them by how often they occur and which tool or model produced them.
The user provides: $ARGUMENTS
This may be:
| Endpoint | Returns |
|---|---|
GET /api/analytics | event_types (counts per type incl. PreToolUse, PostToolUse, APIError), tool_usage (top 20), daily_events (365d) — fleet-wide failure baseline |
GET /api/events?session_id=X | Per-session event stream: event_type, tool_name, summary, data, timestamp — locate APIError and unmatched PreToolUse |
GET /api/sessions?limit=N | Sessions with id, status, model, started_at — pick the recent window and attribute failures to a model |
Resolve $ARGUMENTS to a session set: pull GET /api/sessions?limit=N (default 50, ordered by started_at). Report how many sessions and what time span are covered.
From GET /api/analytics event_types, report total APIError count and the PreToolUse→PostToolUse gap: gap = PreToolUse − PostToolUse (unmatched tool starts = likely failures). State both as raw counts and as a share of total_events.
For each session in scope, pull GET /api/events?session_id=X. Match each PreToolUse to its following PostToolUse by tool_name; unmatched starts are failures. Aggregate failures and APIError events per tool_name. Rank tools by failure frequency (descending).
Join failures to the owning session's model (from GET /api/sessions). Rank models by APIError count and tool-failure count.
List the single most failure-prone tool, the most error-prone model, and the session with the most failures, each with its exact count and one-line summary excerpt from a representative event.
event_type, tool_name, and session_id values — never fabricate counts.curl cannot reach http://localhost:4820, tell the user to start the dashboard with npm start from the repo root.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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