{"uid":"cap_kR5aNHC0wUqXS04hTg7GA","slug":"request-latency-string-normalisation-iqr-outlier-flags-1d7c896e","name":"Request Latency String Normalisation + IQR Outlier Flags","description":"Request latency — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags for supplied request latency records (latency_ms, request_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-software-03-032?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","component","date","latency_ms","request_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"component":{"type":"string","default":"","maxLength":80},"latency_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"request_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","component":"group-a","latency_ms":10,"request_count":1},{"date":"2026-10-02","row_id":"row-2","component":"group-b","latency_ms":20,"request_count":2},{"date":"2026-10-03","row_id":"row-3","component":"group-a","latency_ms":15,"request_count":3},{"date":"2026-10-04","row_id":"row-4","component":"group-b","latency_ms":30,"request_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["latency_ms","request_count"],"type":"string","default":"latency_ms"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_cyw3laV6PYToES5ysa9wX","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Normalises latency_ms or request_count strings and flags IQR-based outliers across up to 200 rows of request latency data, producing two analytical reports per call.","exampleAgentPrompt":"Can you normalise the latency strings and flag IQR outliers in this request latency dataset — I have about 50 rows with latency_ms values across two components, group-a and group-b, from October 2026, and I want to analyse the latency_ms field?","exampleUseCases":[{"title":"API performance anomaly detection","prompt":"I have 120 rows of daily request latency logs for three microservices from last quarter — can you normalise the latency_ms values and flag which rows are IQR outliers so I know which days had abnormal performance?"},{"title":"Request count spike investigation","prompt":"Here are 80 rows of request_count data across two service components from the past month — please normalise the values and use IQR outlier flagging on the request_count field to highlight any traffic spikes."},{"title":"Multi-component latency data cleanup","prompt":"I've got latency records for four service groups covering 150 days and I need both string normalisation and IQR outlier flags on the latency_ms field — can you run both analytical reports on this data?"}],"resultDescription":"Two analytical reports per call: (1) string-normalised versions of the selected field (latency_ms or request_count) per row, and (2) IQR-based outlier flags per row indicating which records fall outside the interquartile range. Results are scoped to the supplied rows (up to 200) with no external data fetched.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, component, latency_ms, request_count) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","All values null for the selected field — IQR computation undefined","Payment not submitted or incorrect amount — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of request latency or request count records and need both string normalisation and IQR-based outlier detection in a single cheap call ($0.01 USDC for two reports). Ideal for lightweight, offline statistical cleaning of performance logs without needing a full analytics platform or live data pipeline. Not suitable when you need prediction, real-time monitoring, datasets larger than 200 rows, or professional performance assessment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:55:48.944Z","isFirstParty":false,"canonicalSlug":"request-latency-string-normalisation-iqr-outlier-flags-1d7c896e"}