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anomaly-alert

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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Anomaly Alert

Detect anomalous sessions in Claude Code Agent Monitor data.

Input

The user provides: $ARGUMENTS

This may be:

  • "all" or empty (default: check all anomaly types)
  • "cost" for cost anomalies only
  • "duration" for duration anomalies only
  • "errors" for error rate anomalies only
  • A sensitivity level: "strict" (1σ), "normal" (2σ), "relaxed" (3σ)

Procedure

  1. Fetch baseline data from http://localhost:4820:

    • GET /api/sessions?limit=500 — historical sessions for baseline
    • GET /api/analytics — aggregated metrics
    • GET /api/pricing/cost — cost data per session
  2. Compute baselines for each metric:

    • Mean, median, standard deviation
    • P25, P75, P90, P95, P99 percentiles
    • Interquartile range (IQR) for robust outlier detection
  3. Detect anomalies using statistical thresholds:

    Cost Anomalies

    • Sessions costing >2σ above mean
    • Single sessions exceeding daily average
    • Sudden cost spikes (session-over-session increase >200%)

    Duration Anomalies

    • Sessions lasting >2σ above mean duration
    • Extremely short sessions (<1 minute) that still incur cost
    • Sessions with unusual active-vs-idle ratios

    Error Rate Anomalies

    • Sessions with error rates >2σ above baseline
    • New error types not seen in previous sessions
    • Sessions with >3 consecutive tool failures

    Behavioral Anomalies

    • Unusual tool combinations not seen before
    • Sessions with abnormally high compaction counts
    • Model switches mid-session (if unexpected)
    • Sessions with no tool usage (pure conversation)

    Token Anomalies

    • Input/output token ratio far from historical norm
    • Cache miss rate significantly higher than average
    • Token usage growing faster than session count
  4. Classify each anomaly:

    • 🔴 Critical: Likely indicates a real problem requiring attention
    • 🟡 Warning: Unusual but may be expected for certain tasks
    • 🔵 Info: Interesting deviation worth noting

Output Format

Present as an Anomaly Report:

═══════════════════════════════════════════════
  ANOMALY DETECTION REPORT
  Analyzed: N sessions | Baseline: last 30 days
  Anomalies found: N (🔴 N critical, 🟡 N warn, 🔵 N info)
═══════════════════════════════════════════════

For each anomaly:

  • Session ID and timestamp
  • Anomaly type and severity
  • Observed value vs expected range
  • Possible explanation
  • Recommended action (if any)

レビュー

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。

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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。

hoangsonww/Claude-Code-Agent-Monitor1,0692026年10月12日 更新

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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。

hoangsonww/Claude-Code-Agent-Monitor1,0692026年10月12日 更新

benchmark

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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.

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。

hoangsonww/Claude-Code-Agent-Monitor1,0692026年10月12日 更新

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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。

hoangsonww/Claude-Code-Agent-Monitor1,0692026年10月12日 更新

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。

hoangsonww/Claude-Code-Agent-Monitor1,0692026年10月12日 更新

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.

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。

hoangsonww/Claude-Code-Agent-Monitor1,0692026年10月12日 更新

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