{"uid":"cap_Q_ZAMx8RVRylMtmeUhMan","slug":"ticket-escalations-iqr-outlier-flags-9facd53b","name":"Ticket Escalations IQR Outlier Flags","description":"Ticket escalations - IQR outlier flags: IQR outlier flags for Ticket escalations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded iqr outlier flags report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-ticket-escalations-outliers?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team","date","tickets","escalations"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"team":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"tickets":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"escalations":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","team":"group-a","row_id":"row-1","tickets":10,"escalations":1},{"date":"2026-10-02","team":"group-b","row_id":"row-2","tickets":20,"escalations":2},{"date":"2026-10-03","team":"group-a","row_id":"row-3","tickets":15,"escalations":3},{"date":"2026-10-04","team":"group-b","row_id":"row-4","tickets":30,"escalations":4}],"maxItems":200,"minItems":1},"field":{"enum":["tickets","escalations"],"type":"string","default":"tickets"}}},"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_TYdE6F3V2diD7ltzLqnBb","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":"Detects IQR-based statistical outliers in ticket escalation records across up to 200 supplied rows, returning a bounded descriptive analytics report.","exampleAgentPrompt":"Can you flag the outlier records in my ticket escalation data? I have rows with date, team, tickets, and escalations — run IQR outlier detection on the escalations field across these records: row-1 (group-a, 2025-01-01, 50 tickets, 2 escalations), row-2 (group-b, 2025-01-02, 48 tickets, 95 escalations), row-3 (group-a, 2025-01-03, 52 tickets, 3 escalations).","exampleUseCases":[{"title":"Spot spikes in team escalations","prompt":"I have daily escalation counts for three support teams over the past month — can you run IQR outlier detection on the escalations field and tell me which rows are statistical anomalies?"},{"title":"Audit ticket volume anomalies","prompt":"Run an IQR outlier check on the tickets field in this dataset: I've got about 80 rows with date, team name, ticket counts, and escalation counts — flag anything that looks statistically unusual."},{"title":"Quality check before escalation reporting","prompt":"Before I send this escalation report to leadership, can you screen the escalations field for outliers using IQR analysis? I'll supply up to 150 records with row IDs, team names, dates, and escalation numbers."}],"resultDescription":"Returns a bounded IQR outlier flags report for each supplied row, indicating which records fall outside the interquartile range for the selected field (tickets or escalations). No forecasting or external enrichment is performed — output is purely local descriptive analytics on the supplied dataset.","failureModes":["Input array empty or missing — returns validation error","More than 200 rows supplied — rejected with maxItems error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Unknown field value other than 'tickets' or 'escalations' — enum validation error","row_id missing or empty — required field violation","Payment not provided or insufficient — x402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need fast, local IQR-based outlier detection on ticket escalation records (up to 200 rows) without any external data enrichment or forecasting. It is specifically scoped to the ticket escalations domain with structured fields for team, date, tickets, and escalations — making it more targeted than a general statistical outlier service. Ideal for pre-reporting audits, anomaly screening, or quality checks on helpdesk data at $0.01 per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T00:52:30.802Z","isFirstParty":false,"canonicalSlug":"ticket-escalations-iqr-outlier-flags-9facd53b"}