Claude-Code-Agent-Monitor

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:
2. GET /api/sessions?limit=500 — historical sessions for baseline
3. GET /api/analytics — aggregated metrics
4. GET /api/pricing/cost — cost data per session

1. Compute baselines for each metric:
2. Mean, median, standard deviation
3. P25, P75, P90, P95, P99 percentiles
4. Interquartile range (IQR) for robust outlier detection

1. 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

1. Classify each anomaly:
2. 🔴 Critical: Likely indicates a real problem requiring attention
3. 🟡 Warning: Unusual but may be expected for certain tasks
4. 🔵 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)