{"uid":"cap_5laLSO8OOjfJeYPM0Nk_p","slug":"kihustle-covariance-calculator-d8531481","name":"KiHustle Covariance Calculator","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/agents/api/v1/covariance-calculator","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"series_a":{"type":"array","items":{"type":"number"}},"series_b":{"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_tf2VO02URtpe79fhuiBDI","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":"Computes the statistical covariance between two numerical data series","exampleAgentPrompt":"Calculate the covariance between these two data series for me — series A is [2.1, 3.5, 4.0, 5.2, 6.1] and series B is [1.8, 2.9, 3.7, 4.5, 5.9].","exampleUseCases":[{"title":"Portfolio risk correlation analysis","prompt":"I have monthly returns for two stocks over the past year. Series A is [0.02, 0.05, -0.01, 0.03, 0.07, -0.02, 0.04, 0.01, 0.06, -0.03, 0.02, 0.05] and series B is [0.01, 0.04, -0.02, 0.02, 0.06, -0.01, 0.03, 0.00, 0.05, -0.02, 0.01, 0.04]. Can you compute the covariance between them so I can assess portfolio risk?"},{"title":"Machine learning feature relationship check","prompt":"Before training my model, I want to check how two features co-vary. Feature A values are [10, 20, 30, 40, 50] and feature B values are [15, 25, 28, 45, 52]. What is the covariance between these two feature arrays?"},{"title":"Sales and ad spend co-movement","prompt":"I have weekly sales figures [500, 520, 610, 580, 700, 750] and corresponding ad spend numbers [100, 110, 140, 130, 160, 180]. Compute the covariance between these two series so I can see how closely they move together."}],"resultDescription":"Returns a JSON object with a 'result' field containing the computed covariance value and a 'status' field confirming success. The covariance scalar indicates the direction and magnitude of linear co-movement between the two input series.","failureModes":["Mismatched array lengths between series_a and series_b may produce an error or undefined result","Empty arrays may cause a computation error","Non-numeric values in either series will likely cause a validation failure","Very large arrays may increase latency beyond typical fast response times","Payment failure (insufficient USDC balance) will block the request entirely"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-call statistical covariance computation without setting up a local math library or data science environment. It is ideal for agents embedded in workflows that occasionally need a quick covariance scalar between two numeric arrays, such as finance bots checking asset co-movement, ML pipelines evaluating feature relationships, or analytics agents comparing paired data sequences.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T07:04:35.749Z","isFirstParty":false}