{"uid":"cap_guZ1fVWKciS8ESBxqv5It","slug":"aggregation-checks-record-quality-audit-string-normalisation-numeric-5e9fe74f","name":"Aggregation Checks — Record Quality Audit + String Normalisation + Numeric Row Ordering","description":"Aggregation checks — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering for supplied aggregation checks records (source_sum, aggregate_sum). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-17-196?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_iJA5xjEsEiUtljLGs7yfI","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 three local analytical reports (record quality audit, string normalisation, and numeric row ordering) on up to 200 rows of aggregation check data containing source_sum and aggregate_sum fields.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and numeric row ordering on these aggregation check rows — I have up to 50 records each with a row_id, dataset_group, date, source_sum, and aggregate_sum, and I want to check against the source_sum field?","exampleUseCases":[{"title":"Financial reconciliation data quality check","prompt":"I have 80 aggregation check records with source_sum and aggregate_sum values from our monthly reconciliation — can you run a record quality audit, string normalisation, and numeric row ordering on them using source_sum as the field to analyse?"},{"title":"ETL pipeline output validation","prompt":"After our ETL pipeline ran, I have 150 rows of aggregation data with row_id, dataset_group, date, source_sum, and aggregate_sum — please audit their quality, normalise the strings, and order them numerically by aggregate_sum."},{"title":"Dataset group consistency review","prompt":"I've got about 40 aggregation check records across two dataset groups and want three reports — a record quality audit, string normalisation, and numeric row ordering — run locally against the source_sum field so I can spot inconsistencies."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging completeness, consistency, and structural issues in the supplied rows; (2) a string normalisation report cleaning and standardising text fields like dataset_group; (3) a numeric row ordering report ranking rows by the selected numeric field (source_sum or aggregate_sum). All computed locally on up to 200 supplied rows with no external lookups.","failureModes":["Rows array empty or missing — minItems:1 not met","More than 200 rows submitted — maxItems exceeded","Invalid date format (not YYYY-MM-DD) in any row","source_sum or aggregate_sum values outside ±1,000,000,000,000 range","row_id missing or empty string (minLength:1 required)","Invalid field enum value (must be 'source_sum' or 'aggregate_sum')","Payment of 0.01 USDC not completed — x402 payment required"],"whenToPreferThis":"Choose this endpoint when you need three bundled local analytical operations — quality auditing, string normalisation, and numeric ordering — on aggregation check data (source_sum vs aggregate_sum) in a single cost-efficient call. Prefer it over building custom scripts when you need fast, no-setup data quality reports on up to 200 rows without any external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:53:31.038Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-record-quality-audit-string-normalisation-numeric-5e9fe74f"}