{"uid":"cap_JGLabyxFAVnj9v3Bc1y2K","slug":"json-outlier-detector-iqr-66ea97ea","name":"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/nexus/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_t83aEVpCk_WN91DNqDAHK","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 statistical outliers in a numeric array using the Interquartile Range (IQR) method","exampleAgentPrompt":"Can you check this list of numbers for outliers using IQR: [12, 14, 13, 100, 15, 11, 14, 200, 13]? I want to know which values are statistically anomalous.","exampleUseCases":[{"title":"Cleaning sensor data before analysis","prompt":"I have a batch of temperature sensor readings — [22.1, 21.8, 22.5, 99.3, 22.0, 21.9, 22.3] — can you run IQR outlier detection on them and tell me which ones look like bad readings?"},{"title":"Flagging unusual sales figures","prompt":"Here are last month's daily sales numbers: [430, 455, 420, 3, 440, 461, 438, 450, 1200, 442]. Can you find which days had statistically anomalous figures using interquartile range detection?"},{"title":"Quality control on user input scores","prompt":"I collected these user engagement scores from a survey: [7, 8, 6, 9, 7, 100, 8, 6, 7, 0]. Can you run IQR-based outlier detection on them so I can decide which responses to exclude?"}],"resultDescription":"Returns a JSON object with a 'result' field indicating processing outcome and a 'status' field ('success' or error). Likely also contains identified outlier positions or values within the numeric array, though the full response schema is minimal.","failureModes":["Empty or missing 'values' array returns an error","Non-numeric values in array may cause processing failure","Extremely small arrays (fewer than 4 values) may produce unreliable IQR results","Malformed JSON body returns a 400-level error","Very large arrays may increase latency"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, stateless IQR-based outlier detection computation over a simple numeric array without setting up a full data science environment. It is well-suited for quick data cleaning, sensor validation, or pre-processing steps in an agent workflow where paying per-call is acceptable and no local statistics library is available.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T19:02:53.083Z","isFirstParty":false}