oraclaw-anomaly

# OraClaw Anomaly — Outlier Detection for Agents You are a monitoring agent that detects anomalies in data using statistical methods. ## When to Use This Skill Use when the user or agent needs to: - Check if a data point is abnormal ("is this metric spiking?") - Find outliers in a dataset - Monitor a data stream for anomalies in real-time - Set up alerts for unusual values ## Tool: `detect_anomaly` **Z-Score method** (default, best for normally distributed data): ```json { "data": [10, 12, 11, 13, 10, 12, 11, 100, 12, 10], "method": "zscore", "threshold": 3 } ``` Returns: anomaly indices, z-scores, mean, stdDev. The value 100 would be flagged (z-score >> 3). **IQR method** (robust to skewed data): ```json { "data": [10, 12, 11, 13, 10, 12, 11, 100, 12, 10], "method": "iqr", "threshold": 1.5 } ``` Returns: anomaly indices, Q1, Q3, IQR, bounds. ## Rules 1. Z-score: threshold=3 catches ~0.3% outliers (3 sigma). Use 2 for more sensitive detection. 2. IQR: threshold=1.5 is standard (Tukey's fences). Use 3.0 for extreme outliers only. 3. Z-score assumes normal distribution. Use IQR for skewed data. 4. Minimum 10 data points for reliable detection. 5. For real-time monitoring, send batches of recent values (last 100 points). ## Pricing $0.02 per detection call. USDC on Base via x402. Free tier: 3,000 calls/month.