- 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
- primaryEnv
- ORACLAW_API_KEY
- emoji
- 🚨
- homepage
- https://oraclaw.dev/anomaly
- tags
- 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
- Z-score: threshold=3 catches ~0.3% outliers (3 sigma). Use 2 for more sensitive detection.
- IQR: threshold=1.5 is standard (Tukey's fences). Use 3.0 for extreme outliers only.
- Z-score assumes normal distribution. Use IQR for skewed data.
- Minimum 10 data points for reliable detection.
- 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.