KiHustle JSON Outlier Detector (IQR)

x402

KiHustle JSON Outlier Detector (IQR) is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-13).

Detects statistical outliers in a numeric array using the Interquartile Range (IQR) method

Price / call$0.002

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Success—

StatusUnknown

How this service works

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Output

Returns a JSON object indicating which values in the input array are outliers as detected by the IQR method, along with a status field confirming successful processing. The response includes a 'result' field (processed) and 'status' (success), with outlier information derived from the IQR computation.

Example use cases

Clean sensor data before analysis

“I have a stream of temperature readings from my IoT sensors and I suspect a few are bad readings — can you run IQR outlier detection on these values and tell me which ones to throw out: [22.1, 21.8, 22.4, 99.3, 21.9, 22.0, 22.3, -50.1, 21.7]?”

Flag suspicious revenue figures

“Here are daily revenue numbers from last month — can you check which ones are statistical outliers using the IQR method so I can investigate them further: [1200, 1350, 1100, 1280, 8900, 1190, 1310, 50, 1260, 1400]?”

Validate survey response scores

“I collected Likert scale scores from 10 respondents and want to find any that look like data entry errors — please run IQR outlier detection on: [3, 4, 3, 5, 4, 3, 4, 100, 4, 3].”

Technical details

Endpoint

POSThttps://kihustle.tech/bot-bazaar/api/v1/json-outlier-detector-iqr?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a lightweight, cost-effective statistical outlier detection step in a data pipeline and prefer IQR-based (robust, non-parametric) detection over z-score or ML-based approaches. It is ideal for quick data cleaning tasks, validating sensor readings, or flagging anomalous numeric values in small-to-medium datasets without standing up your own statistics library.

Side effects

Empty or missing 'values' array results in an error
Non-numeric values in the array may cause processing failure
Insufficient data points (fewer than 4) may make IQR computation unreliable
Payment failure via x402 protocol returns 402 status
Malformed JSON body returns 400-level error

Input

Schema · Request body

Field	Type	Description
values	array

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": "processed",
    "status": "success"
  }
}

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