Audit hook delivery health from Agent Monitor data — balance PreToolUse vs PostToolUse (a gap means tools that started but never reported back), detect missing Stop/SubagentStop terminators (sessions/subagents that never closed), and check for stale ingestion (no recent events). Use when hooks look unreliable or events seem to be dropping.
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Assess whether the hook pipeline is delivering events reliably, using the event counts and stream the dashboard already has. This is about delivery health (missing/dropped events), not about why a model errored.
The user provides: $ARGUMENTS
This may be:
| Endpoint | Returns |
|---|---|
GET /api/analytics | event_types (counts per type: PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd), daily_events (365d), total_subagents, sessions_by_status — fleet-wide delivery balance |
GET /api/stats | total_sessions, total_agents, total_events, events_today — expected terminator counts and recency |
GET /api/events?session_id=X | Per-session stream — confirm which sessions are missing a PostToolUse, Stop, or SubagentStop |
From GET /api/analytics event_types: gap = PreToolUse − PostToolUse. A positive gap means tools whose completion hook never arrived. Report the gap as a count and as a percentage of PreToolUse. A healthy pipeline keeps this near 0%.
Compare Stop count against completed sessions (sessions_by_status) and SubagentStop against total_subagents/total_agents (from /api/stats). A shortfall means sessions or subagents that ran but never emitted a closing hook — likely dropped delivery or a crashed handler. Report expected vs observed for each.
Check events_today from /api/stats and the tail of daily_events from analytics. If recent days are empty while sessions exist, ingestion has stalled. Report the most recent day with events and how long ago that was.
For the sessions with the largest gaps or missing terminators, pull GET /api/events?session_id=X and confirm which specific hook types are absent. List the offending session IDs.
event_type counts and session_id values — never fabricate.npm start).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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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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