{"uid":"cap_eWQ7pw8XWPOp87VJXMGf4","slug":"aggregation-checks-string-normalisation-numeric-row-ordering-afa49110","name":"Aggregation Checks — String Normalisation + Numeric Row Ordering","description":"Aggregation checks — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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-17-025?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_CmYJwrZIbrPKyvt9ugsg2","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":"Normalises string fields and orders rows numerically for up to 200 supplied aggregation check records containing source_sum and aggregate_sum values, producing 2 independent analytical reports.","exampleAgentPrompt":"Run string normalisation and numeric row ordering on my aggregation check data — I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, source_sums of 10, 20, 15, 30 and aggregate_sums of 1, 2, 3, 4. Use source_sum as the field and give me both reports.","exampleUseCases":[{"title":"Reconciling financial dataset group sums","prompt":"I have 50 reconciliation rows across three dataset groups with source_sum and aggregate_sum values for each day in October — can you normalise the string labels and order the rows numerically by source_sum and produce both analytical reports?"},{"title":"Detecting ordering anomalies in aggregation data","prompt":"Here are 120 rows of aggregation check data with row IDs, dates, and both source_sum and aggregate_sum values — run numeric row ordering on them so I can spot where the sequence breaks down, and give me the two reports back."},{"title":"Cleaning group labels before data pipeline import","prompt":"Before I import my aggregation records into the pipeline, normalise the dataset_group strings and sort the 80 rows by aggregate_sum so everything is consistently ordered — I'll paste the rows in the request."}],"resultDescription":"Two independent analytical reports covering (1) string normalisation of the supplied row fields and (2) numeric ordering of the rows, applied to up to 200 input records each containing row_id, dataset_group, date, source_sum, and aggregate_sum.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, dataset_group, date, source_sum, aggregate_sum) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside ±1,000,000,000,000 range — schema rejection","Invalid field enum value (not 'source_sum' or 'aggregate_sum') — bad request","Payment of 0.01 USDC not received — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation and numeric row ordering on batch aggregation check data (up to 200 rows) without any external lookups or live data — particularly for data reconciliation workflows comparing source_sum against aggregate_sum across dataset groups. It covers two analytical angles in a single $0.01 call, making it cost-efficient for routine data quality checks.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:31.102Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-string-normalisation-numeric-row-ordering-afa49110"}