{"uid":"cap_qFaXCY2-4WtSmkfXlLeyw","slug":"kihustle-json-outlier-detector-iqr-db76ff20","name":"KiHustle JSON Outlier Detector (IQR)","description":"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.","url":"https://kihustle.tech/api/v1/json-outlier-detector-iqr","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"values":{"type":"array","items":{"type":"number"}}}},"responseSchema":{"type":"json","example":{"result":"processed","status":"success"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_QQ_w9I9KEAtbOGG_rCun7","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Detects outliers in a numeric array using the Interquartile Range (IQR) statistical method","exampleAgentPrompt":"Can you check this list of numbers for statistical outliers using IQR: [12, 15, 14, 10, 100, 13, 11, 200, 14, 12]?","exampleUseCases":[{"title":"Cleaning sensor data before processing","prompt":"I have temperature readings from my IoT sensors: [22.1, 21.8, 22.3, 99.5, 21.9, 22.0, 0.1, 22.4] — can you detect which ones are outliers so I can clean the dataset before analysis?"},{"title":"Flagging suspicious financial figures","prompt":"Here are monthly revenue figures in dollars: [4200, 4350, 4100, 4500, 42000, 4250, 4300] — can you identify which values are statistical outliers using IQR so I can investigate them?"},{"title":"Validating user-submitted survey scores","prompt":"I collected these survey response scores from users: [7, 8, 6, 9, 7, 8, 100, 6, 7, -50, 8] — can you run outlier detection on them so I know which ones to exclude before computing averages?"}],"resultDescription":"Returns a JSON object indicating the processing result and success status. The response confirms which values were identified as outliers or provides a processed result flag along with a success status field.","failureModes":["Empty array input may return an error or undefined result","Non-numeric values in the array may cause processing failure","Null or missing 'values' field will likely return a 400-level error","Very small arrays (fewer than 4 elements) may not produce meaningful IQR results","Response schema is minimal; detailed outlier indices or bounds may not be explicitly enumerated"],"whenToPreferThis":"Choose this endpoint when you need a simple, cheap ($0.002/call) statistical outlier detection step over a flat numeric array and do not need a full ML-based anomaly detection pipeline. It is ideal for lightweight data cleaning, quick sanity checks on numeric datasets, or preprocessing steps in an automated workflow where IQR-based detection is sufficient.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:45:45.160Z","isFirstParty":false}