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.
89 lines
3.1 KiB
Markdown
89 lines
3.1 KiB
Markdown
---
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description: >
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Identify anomalous sessions using Agent Monitor data — cost outliers from
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the pricing engine, token anomalies (cache miss spikes, compaction baseline
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surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError
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clusters), behavioral deviations from workflow intelligence (complexity
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score outliers, error propagation anomalies), and sessions with abnormal
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metadata (extreme turn_count, high thinking_blocks, zero turn_duration).
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---
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# Anomaly Alert
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Detect anomalous sessions in Claude Code Agent Monitor data.
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## Input
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The user provides: **$ARGUMENTS**
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This may be:
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- "all" or empty (default: check all anomaly types)
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- "cost" for cost anomalies only
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- "duration" for duration anomalies only
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- "errors" for error rate anomalies only
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- A sensitivity level: "strict" (1σ), "normal" (2σ), "relaxed" (3σ)
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## Procedure
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1. **Fetch baseline data** from `http://localhost:4820`:
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- `GET /api/sessions?limit=500` — historical sessions for baseline
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- `GET /api/analytics` — aggregated metrics
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- `GET /api/pricing/cost` — cost data per session
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2. **Compute baselines** for each metric:
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- Mean, median, standard deviation
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- P25, P75, P90, P95, P99 percentiles
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- Interquartile range (IQR) for robust outlier detection
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3. **Detect anomalies** using statistical thresholds:
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### Cost Anomalies
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- Sessions costing >2σ above mean
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- Single sessions exceeding daily average
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- Sudden cost spikes (session-over-session increase >200%)
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### Duration Anomalies
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- Sessions lasting >2σ above mean duration
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- Extremely short sessions (<1 minute) that still incur cost
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- Sessions with unusual active-vs-idle ratios
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### Error Rate Anomalies
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- Sessions with error rates >2σ above baseline
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- New error types not seen in previous sessions
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- Sessions with >3 consecutive tool failures
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### Behavioral Anomalies
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- Unusual tool combinations not seen before
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- Sessions with abnormally high compaction counts
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- Model switches mid-session (if unexpected)
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- Sessions with no tool usage (pure conversation)
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### Token Anomalies
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- Input/output token ratio far from historical norm
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- Cache miss rate significantly higher than average
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- Token usage growing faster than session count
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4. **Classify each anomaly**:
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- **🔴 Critical**: Likely indicates a real problem requiring attention
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- **🟡 Warning**: Unusual but may be expected for certain tasks
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- **🔵 Info**: Interesting deviation worth noting
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## Output Format
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Present as an **Anomaly Report**:
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```
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═══════════════════════════════════════════════
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ANOMALY DETECTION REPORT
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Analyzed: N sessions | Baseline: last 30 days
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Anomalies found: N (🔴 N critical, 🟡 N warn, 🔵 N info)
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═══════════════════════════════════════════════
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```
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For each anomaly:
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- Session ID and timestamp
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- Anomaly type and severity
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- Observed value vs expected range
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- Possible explanation
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- Recommended action (if any)
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