nntrivi2001
57dc91585d
Internal SmartGift build of a Claude Code monitoring dashboard. Lanes: a durable unit of parallel agent work, one per working directory, tracked across session restarts. Managed lanes are git worktrees the dashboard provisions and can reset or remove behind a three-check destroy guard and a counted preflight; adopted lanes are directories you already own and are never destroyable. Pipelines: a lane moves through pipeline stages. A stage the agent declares with evidence renders green; a stage inferred from the tool-event stream renders dashed amber and never counts as done. Detection is forward-only within a 30-minute window, and never writes the declared stage. Workspace: one page at /run with a lane grid, the selected lane's pipeline, and a full Claude console behind a disclosure.
3.1 KiB
3.1 KiB
description
| description |
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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). |
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
-
Fetch baseline data from
http://localhost:4820:GET /api/sessions?limit=500— historical sessions for baselineGET /api/analytics— aggregated metricsGET /api/pricing/cost— cost data per session
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Compute baselines for each metric:
- Mean, median, standard deviation
- P25, P75, P90, P95, P99 percentiles
- Interquartile range (IQR) for robust outlier detection
-
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
-
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)