SignalDesk AI IQR Outlier Detection

x402

SignalDesk AI IQR Outlier Detection is a paid API for AI agents from x402-agent.kariguetoazul.workers.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-13).

Detects statistical outliers in a numeric dataset using the interquartile range (IQR) method, returning quartiles, IQR, fence bounds, and the list of outlying values.

Price / call$0.001

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StatusUnknown

How this service works

Evidence-backed autonomous AI jobs and paid data tools. Queue/D1/cron run outside the chat; free quotas and bounded execution apply.

Output

Returns a JSON object containing: q1 (first quartile), q3 (third quartile), iqr (interquartile range), lower (lower fence = Q1 - 1.5*IQR), upper (upper fence = Q3 + 1.5*IQR), and outliers (array of values from the input that fall outside the fences).

Example use cases

Sensor anomaly detection in IoT pipeline

“I have a list of temperature readings from my IoT sensors — can you run IQR outlier detection on them and tell me which readings are anomalous? Values: [22.1, 21.8, 22.5, 23.0, 21.9, 99.7, 22.3, 21.7, 22.0].”

Financial price spike flagging

“Here are daily closing prices for a stock over the past month: [150, 152, 149, 151, 153, 148, 302, 150, 151, 149]. Can you use IQR to find which prices are statistical outliers?”

Test score quality control

“I have student exam scores and want to flag any suspiciously high or low values before reporting: [72, 68, 75, 71, 69, 74, 4, 73, 70, 68, 200, 71]. Which ones are outliers based on IQR?”

Technical details

Endpoint

POSThttps://x402-agent.kariguetoazul.workers.dev/api/stats/iqr-outliers?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a fast, low-cost, stateless IQR-based outlier computation on a raw numeric array of up to 1000 values. It is ideal for agent pipelines that need to flag anomalies before further processing, or for data cleaning steps in automated workflows. Prefer this over a full statistical library call when operating within a serverless or agent environment that requires a pay-per-use micropayment model. Not suitable for streaming data, time-series-aware outlier detection, or datasets exceeding 1000 observations.

Side effects

Empty or missing 'values' array returns validation error
Array exceeds 1000 items — request rejected with size error
Non-numeric or non-finite values in the array may cause rejection or NaN results
Payment not provided or insufficient USDC balance returns HTTP 402
Values outside ±1,000,000,000,000,000 range rejected by schema validation
Single-element arrays may produce degenerate IQR=0 results

Input

Schema · Request body

Field	Type	Description
values	array	Up to 1000 finite observations.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "q1": 2,
    "q3": 4,
    "iqr": 2,
    "lower": -1,
    "upper": 7,
    "outliers": [
      100
    ]
  }
}

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