{"uid":"cap_jtNu3X8_b61ypNevAqVb-","slug":"time-entries-iqr-outlier-flags-ee9eb94a","name":"Time Entries IQR Outlier Flags","description":"Time entries - IQR outlier flags: IQR outlier flags for Time entries. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded iqr outlier flags deliverable.","url":"https://market.datapackvibe.com/x402/assistant-sector-analytics-time-entries-outliers?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","project","date","hours","billable_hours"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"project":{"type":"string","default":"","maxLength":80},"billable_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","hours":10,"row_id":"row-1","project":"group-a","billable_hours":1},{"date":"2026-10-02","hours":20,"row_id":"row-2","project":"group-b","billable_hours":2},{"date":"2026-10-03","hours":15,"row_id":"row-3","project":"group-a","billable_hours":3},{"date":"2026-10-04","hours":30,"row_id":"row-4","project":"group-b","billable_hours":4}],"maxItems":200,"minItems":1},"field":{"enum":["hours","billable_hours"],"type":"string","default":"hours"}}},"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_9GiFoQ56oz427FNC3lo6w","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 statistical outliers in time entry records (hours or billable_hours) using the Interquartile Range (IQR) method across up to 200 supplied rows.","exampleAgentPrompt":"Can you run IQR outlier detection on these time entries and flag any rows where the hours field looks statistically anomalous? I have about 50 rows with row_id, project, date, hours, and billable_hours.","exampleUseCases":[{"title":"Timesheet fraud detection","prompt":"I have 150 employee timesheet rows with hours logged per day — can you flag any entries where the hours value is an IQR outlier so I can review them for potential padding or errors?"},{"title":"Billable hours anomaly review","prompt":"Before I send client invoices, can you check these 80 time entry records and flag any billable_hours values that are statistical outliers using IQR analysis?"},{"title":"Project data quality audit","prompt":"I'm cleaning up our project time logs — please run IQR outlier flagging on these time entries using the hours field so I can identify and investigate the suspicious rows before importing them into our billing system."}],"resultDescription":"Returns each submitted time entry row annotated with an IQR outlier flag indicating whether its numeric value (hours or billable_hours) falls outside the interquartile range bounds, enabling identification of statistically anomalous records for review or exclusion.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) — schema validation failure","Missing required fields (row_id, project, date, hours, billable_hours) — rejected row or error","All values identical — IQR is zero, potentially all or no rows flagged","Null numeric values in the selected field — may be excluded from calculation or flagged separately"],"whenToPreferThis":"Choose this endpoint when you need fast, local statistical outlier detection on time entry records without external enrichment or machine learning. Ideal for data quality audits, timesheet validation, and billing anomaly detection on small to medium batches (up to 200 rows) where IQR-based flagging is the appropriate method and cost efficiency ($0.01/call) matters.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T19:07:41.109Z","isFirstParty":false,"canonicalSlug":"time-entries-iqr-outlier-flags-ee9eb94a"}