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".
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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).
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。