KiHustle JSON Box Plot Stats

x402

KiHustle JSON Box Plot Stats is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-21).

Computes box plot statistics (quartiles, median, min, max, outliers) from an array of numeric values

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.

Output

Returns a JSON object with box plot statistics derived from the input numeric array, including status and result fields. Expected outputs include quartile values (Q1, Q2/median, Q3), minimum, maximum, interquartile range (IQR), and potentially outlier identification — though the exact schema is minimal and only confirms 'processed' and 'success' status.

Example use cases

Outlier detection in sales data

“I have a list of daily sales figures for last month — [120, 135, 98, 4, 142, 130, 5, 115, 128, 200, 131, 119] — can you run box plot stats on them so I can see which days were outliers?”

Student score distribution analysis

“Here are the test scores from my class: [55, 62, 70, 71, 73, 75, 80, 82, 85, 88, 90, 92, 95, 100, 45]. Can you calculate the five-number summary and quartiles for me?”

Response time spread for monitoring

“I've got API response times in milliseconds: [210, 220, 195, 230, 215, 800, 205, 225, 218, 212, 750, 200]. Run box plot statistics on these so I can see the median and whether those high values are real outliers.”

Technical details

Endpoint

POSThttps://kihustle.tech/nexus/api/v1/json-box-plot-stats?utm_source=zero.xyz

When to prefer

Use this endpoint when you need a lightweight, pay-per-call API to compute box plot statistics from a numeric array without setting up a local statistics library. It is suitable for agents that periodically need to summarize datasets, detect outliers, or prepare chart-ready descriptive statistics on demand.

Side effects

Empty or missing 'values' array may return an error or undefined stats
Non-numeric values in the array may cause processing failure
Very large arrays may exceed payload limits
Malformed JSON input body returns a 400-level error
Single-element arrays may produce degenerate statistics

Input

Schema · Request body

Field	Type	Description
values	array

Output

Schema · Response

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

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