{"uid":"cap_MG5OaWnKFcIgY5ErTzHOJ","slug":"travel-administration-string-normalisation-and-date-by-category-pivot-7f7908f9","name":"Travel Administration String Normalisation and Date-by-Category Pivot","description":"Travel administration — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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-14-035?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"}}},"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_Jvaoc8UsZvoRDMhT63nb6","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 two local analytical reports on travel administration records: string normalisation and a date-by-category pivot table, for up to 200 rows of trip_count or recorded_cost data.","exampleAgentPrompt":"Can you run string normalisation and a date-by-category pivot on my travel admin records? I have up to 50 rows with team groups like 'group-a' and 'group-b', dates in YYYY-MM-DD format, and I want to analyse trip_count.","exampleUseCases":[{"title":"Monthly travel cost pivot by team","prompt":"I have about 120 rows of travel records with team groups and dates from last quarter — can you normalise the team group strings and give me a pivot table of recorded_cost broken down by date and category?"},{"title":"Trip count analysis across departments","prompt":"Run a date-by-category pivot on these 80 travel admin rows using trip_count as the metric — I need to see how each team group compares across different dates, and please normalise the string fields too."},{"title":"Cleaning and summarising travel data export","prompt":"I exported 150 rows of travel administration data with team_group, date, trip_count, and recorded_cost fields — can you normalise the strings and pivot the trip_count values by date and team group so I can spot patterns?"}],"resultDescription":"Returns two analytical reports computed locally: (1) a string-normalised version of the input records (standardising team_group and other string fields), and (2) a pivot table cross-tabulating the selected numeric field (trip_count or recorded_cost) by date and team_group category — all derived from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, team_group, date, trip_count, or recorded_cost) cause validation errors","Date strings not matching YYYY-MM-DD pattern rejected by schema validation","Numeric values outside the allowed range (-1e12 to 1e12) rejected","Invalid field enum value (not 'trip_count' or 'recorded_cost') causes error","Empty rows array rejected (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of travel administration records and need two lightweight, offline analytical transformations in one call: string normalisation of categorical fields and a date-by-category pivot of a numeric measure. It is ideal for quick data cleaning and cross-tabulation without requiring external data sources, live lookups, or predictive models. At $0.01 per call it is cost-effective for small-to-medium batch analytical tasks on travel cost or trip count data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:47:49.037Z","isFirstParty":false,"canonicalSlug":"travel-administration-string-normalisation-and-date-by-category-pivot-7f7908f9"}