{"uid":"cap_BglQm615dkrv7vhnnuRki","slug":"travel-administration-iqr-outlier-flags-keyed-dataset-comparison-1cb3fadd","name":"Travel Administration IQR Outlier Flags + Keyed Dataset Comparison","description":"Travel administration — IQR outlier flags + Keyed dataset comparison: IQR outlier flags + Keyed dataset comparison for supplied travel administration records (trip_count, recorded_cost). 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-operations-14-179?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","trip_count","recorded_cost"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"trip_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"recorded_cost":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","trip_count":10,"recorded_cost":1},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","trip_count":20,"recorded_cost":2},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","trip_count":15,"recorded_cost":3},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","trip_count":30,"recorded_cost":4}],"maxItems":200,"minItems":1},"field":{"enum":["trip_count","recorded_cost"],"type":"string","default":"trip_count"},"other":{"type":"array","items":{"type":"object","required":["row_id","team_group","date","trip_count","recorded_cost"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"team_group":{"type":"string","default":"","maxLength":80},"trip_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"recorded_cost":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","team_group":"group-a","trip_count":10,"recorded_cost":1},{"date":"2026-10-02","row_id":"row-2","team_group":"group-b","trip_count":21,"recorded_cost":2},{"date":"2026-10-03","row_id":"row-3","team_group":"group-a","trip_count":17,"recorded_cost":3},{"date":"2026-10-04","row_id":"row-4","team_group":"group-b","trip_count":33,"recorded_cost":4}],"maxItems":200,"minItems":0}}},"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_mM6dK7ZAgckY9QwVFh3ma","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":"Runs IQR-based outlier detection and keyed dataset comparison on travel administration records (trip_count, recorded_cost) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Check our travel records for IQR outliers in recorded_cost and compare them against last month's dataset — here are two sets of up to 200 rows with row_id, team_group, date, trip_count, and recorded_cost fields.","exampleUseCases":[{"title":"Expense anomaly detection for travel team","prompt":"I have two months of travel records for our teams — each row has a row_id, team_group, date, trip_count, and recorded_cost. Can you flag any IQR outliers in recorded_cost and also compare this month's data against last month's keyed by row_id to see what changed?"},{"title":"Auditing trip count anomalies by group","prompt":"Run an IQR outlier check on the trip_count field across these 150 travel records grouped by team, and also do a keyed comparison against our baseline dataset to highlight any discrepancies."},{"title":"Before-and-after travel data reconciliation","prompt":"We updated our travel administration records and I need to know which rows changed — please compare the old and new datasets using the keyed comparison report, and also flag any trip_count outliers using IQR in the new data."}],"resultDescription":"Two analytical reports: (1) IQR outlier flags identifying which rows have trip_count or recorded_cost values outside the interquartile range bounds, and (2) a keyed dataset comparison highlighting differences between the two supplied datasets matched by row_id.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, trip_count, recorded_cost) cause validation errors","Date field not matching YYYY-MM-DD pattern returns schema error","Fewer than 1 row supplied causes minItems validation failure","Null values in numeric fields may affect IQR calculation accuracy"],"whenToPreferThis":"Choose this endpoint when you have travel administration data (trip counts, recorded costs) and need both statistical outlier detection via IQR and a structured row-by-row comparison between two datasets in a single low-cost call. Ideal for auditing travel expense records, detecting anomalies, or reconciling updated datasets without needing external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:51:22.290Z","isFirstParty":false,"canonicalSlug":"travel-administration-iqr-outlier-flags-keyed-dataset-comparison-1cb3fadd"}