oraclaw-anomaly

name	oraclaw-anomaly
description	Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.
version	1.0.0
metadata	{"openclaw":{"requires":{"env":["ORACLAW_API_KEY"]},"primaryEnv":"ORACLAW_API_KEY","emoji":"🚨","homepage":"https://web-olive-one-89.vercel.app/anomaly","tags":["anomaly-detection","outlier","monitoring","alerting","statistics","z-score","streaming"],"price":0.02,"currency":"USDC"}}

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):

{
  "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):

{
  "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.