{"uid":"cap_chtvg_LMRAnEzIRwlqtwW","slug":"kihustle-json-outlier-detector-z-score-92d3939a","name":"KiHustle 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/nexus/api/v1/json-outlier-detector-zscore","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_d59lA6u6pKf6SBp_aG2xp","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 JSON numeric data using Z-score analysis","exampleAgentPrompt":"Can you run a Z-score outlier detection on this dataset: [12, 14, 13, 15, 100, 12, 11, 14] and tell me which values are statistical anomalies?","exampleUseCases":[{"title":"Sales data anomaly detection","prompt":"I have monthly sales figures as JSON — can you run Z-score outlier detection on them and flag any months that look statistically abnormal? Here's the data: [4200, 4350, 4100, 4500, 12000, 4250, 4300]"},{"title":"Sensor reading quality check","prompt":"These are temperature sensor readings from my IoT device: [22.1, 21.9, 22.3, 22.0, 99.5, 21.8, 22.2] — please detect any outliers using Z-score so I can filter bad readings before processing."},{"title":"Financial transaction screening","prompt":"Can you analyze this list of transaction amounts for statistical outliers using Z-score? Values: [150, 175, 160, 155, 9800, 162, 158]. I want to know which ones deviate significantly from the norm."}],"resultDescription":"Returns a JSON object with a result field indicating processed status and a success status flag. Expected to include Z-score computations and flagged outlier values within the dataset, though exact output fields may vary.","failureModes":["Malformed or non-numeric text input returns error or unexpected results","Empty input produces no meaningful outlier analysis","Non-JSON text string may cause parsing failure","Very small datasets (fewer than 3 values) may yield unreliable Z-scores","Missing or null text field returns validation error"],"whenToPreferThis":"Choose this endpoint when you need a quick, low-cost ($0.002/call) statistical outlier detection pass on a numeric dataset encoded as text/JSON, especially in lightweight automation pipelines where you don't want to spin up a full data science environment. Best for single-shot anomaly checks on small-to-medium arrays.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T19:03:06.345Z","isFirstParty":false}