{"uid":"cap_xTdYNbZh65aMD7xFnRSnV","slug":"aggregation-checks-string-normalisation-date-by-category-pivot-6c49d5f3","name":"Aggregation Checks — String Normalisation + Date by Category Pivot","description":"Aggregation checks — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied aggregation checks records (source_sum, aggregate_sum). 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-analytics-17-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_sum","aggregate_sum"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_sum":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"aggregate_sum":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_sum":10,"aggregate_sum":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_sum":20,"aggregate_sum":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_sum":15,"aggregate_sum":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_sum":30,"aggregate_sum":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["source_sum","aggregate_sum"],"type":"string","default":"source_sum"}}},"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_akgTOcQCcwhpuPXpcGk5K","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":"Performs string normalisation and date-by-category pivot analysis on up to 200 aggregation check records comparing source_sum and aggregate_sum values, returning 2 independent analytical reports.","exampleAgentPrompt":"Can you run string normalisation and a date-by-category pivot on these aggregation check records — I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and I want to analyze the source_sum field?","exampleUseCases":[{"title":"ETL pipeline reconciliation check","prompt":"I need to reconcile my ETL output — here are 150 rows with row IDs, dates, dataset groups, and both a source_sum and aggregate_sum. Can you normalise the string fields and pivot the data by date and category so I can spot where the totals diverge?"},{"title":"Monthly financial aggregation audit","prompt":"Run a string normalisation and date-by-category pivot on my monthly aggregation check data — I have 80 rows across groups 'revenue' and 'costs' with dates in October 2026, and I want to analyse the aggregate_sum field for each group."},{"title":"Data warehouse group comparison","prompt":"I have 200 rows of aggregation checks from our data warehouse covering two dataset groups, each with a date, source_sum, and aggregate_sum. Can you normalise the strings and give me a pivot of source_sum values broken down by category and date?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report that standardises text fields across the supplied rows, and (2) a date-by-category pivot table that groups and summarises the selected numeric field (source_sum or aggregate_sum) by dataset_group and date — all computed locally on the supplied data with no external lookups.","failureModes":["Rows array empty or missing — returns validation error","More than 200 rows submitted — rejected with limit error","Invalid date format (not YYYY-MM-DD) — schema validation failure","source_sum or aggregate_sum out of range ±1,000,000,000,000 — rejected","field enum value not 'source_sum' or 'aggregate_sum' — schema error","row_id missing or exceeds 80 characters — validation failure"],"whenToPreferThis":"Choose this endpoint when you need a quick, low-cost ($0.01 USDC) dual-report analysis combining string normalisation with a date-by-category pivot on up to 200 aggregation check records — particularly for ETL reconciliation, data warehouse audits, or financial aggregation QA where no live external data lookup is needed and you want two complementary views in a single call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:08.934Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-string-normalisation-date-by-category-pivot-6c49d5f3"}