{"uid":"cap_HoxOVOGyON0KNk-UfeIDD","slug":"json-outlier-detector-z-score-6980c0f2","name":"JSON Outlier Detector (Z-Score)","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-zscore?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string"}}},"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_-FSx2Z0VnuacEKfi0dQ3K","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 JSON data using Z-score statistical analysis","exampleAgentPrompt":"Can you run a Z-score outlier detection on this dataset: [12, 13, 14, 100, 13, 12, 15, 11] and tell me which values are statistical anomalies?","exampleUseCases":[{"title":"Flagging sensor data anomalies","prompt":"I have a list of temperature sensor readings — can you run Z-score outlier detection on this JSON to find any unusual spikes or drops: [22.1, 22.4, 22.0, 21.9, 87.3, 22.2, 21.8]?"},{"title":"Cleaning financial transaction data","prompt":"Before I process these transaction amounts, can you check this dataset for statistical outliers using Z-score so I can flag anything that looks suspicious: [150, 145, 160, 155, 4500, 148, 152]?"},{"title":"QA check on survey response scores","prompt":"I've got survey scores from 50 respondents and I want to make sure there are no weird outlier entries before I analyze the results — can you run Z-score outlier detection on this JSON data for me?"}],"resultDescription":"Returns a JSON object with a result field indicating whether outliers were detected and a status field confirming success. May include Z-score values or flagged data points depending on the input.","failureModes":["Malformed or non-numeric input text causes processing errors","Empty or null text field returns error","Non-JSON or unparseable data in text field may return failure status","Dataset too small to compute meaningful Z-scores","Network timeout on large datasets"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, inexpensive ($0.002/call) statistical outlier check on numeric JSON data using the well-established Z-score method. Best suited for quick data quality checks, preprocessing pipelines, or anomaly flagging before further analysis. Prefer this over ML-based anomaly detectors when you need a simple, interpretable, threshold-based statistical approach.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-27T13:05:41.769Z","isFirstParty":false,"canonicalSlug":"json-outlier-detector-z-score-dbe9b8f8"}