{"uid":"cap_ij7CB2YAOye0x3cpJEF4W","slug":"kihustle-correlation-matrix-builder-66fea911","name":"KiHustle Correlation Matrix Builder","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/correlation-matrix-builder","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"datasets":{"type":"object"}}},"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_T6OfeGm2Nov8vnclJDepY","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 a correlation matrix from submitted datasets to reveal statistical relationships between variables","exampleAgentPrompt":"Can you build a correlation matrix for my dataset — I have columns for revenue, ad spend, user count, and churn rate across 12 months and want to see how they all relate to each other?","exampleUseCases":[{"title":"Feature selection for ML model","prompt":"I'm building a machine learning model and need to identify which features are redundant — can you compute a correlation matrix for my dataset with columns price, volume, sentiment_score, days_listed, and conversion_rate so I can drop the highly correlated ones?"},{"title":"Marketing channel performance analysis","prompt":"I have monthly data for email opens, social clicks, paid impressions, and total sales — can you run a correlation matrix on it so I can see which marketing channels are most tied to revenue?"},{"title":"Financial variable relationship check","prompt":"I've got a dataset with stock return, market index, interest rate, and volatility across 24 months — please build me a correlation matrix so I understand how these financial variables move together."}],"resultDescription":"Returns a JSON object with a 'result' field indicating the processed correlation matrix and a 'status' field confirming success. The correlation matrix contains pairwise correlation coefficients between all input dataset variables, typically in the range of -1 to 1.","failureModes":["Malformed or empty datasets object returns a generic error response","Non-numeric data in datasets may cause computation failure or silent errors","Missing required 'datasets' field results in processing failure","Very large datasets may time out or produce incomplete results","Sparse or misaligned datasets may yield unreliable correlation values"],"whenToPreferThis":"Use this endpoint when you need a quick, low-cost ($0.002 per call) correlation matrix computation over multiple numerical variables. It suits lightweight statistical analysis workflows where you need pairwise correlation coefficients without spinning up a full data science environment. Prefer alternatives if you need advanced statistical outputs (p-values, confidence intervals), large-scale batch processing, or detailed schema validation on inputs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T19:02:59.823Z","isFirstParty":false}