{"uid":"cap_0opuFE-ndwP-HgAy_3F3X","slug":"aggregation-checks-string-normalisation-numeric-distribution-bb053b8f","name":"Aggregation Checks — String Normalisation + Numeric Distribution","description":"Aggregation checks — String normalisation + Numeric distribution: String normalisation + Numeric distribution 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-17-022?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_HQwtpGYyB33HUb1bLxI7w","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":"Analyzes up to 200 aggregation check records by performing string normalisation on group fields and numeric distribution analysis on source_sum or aggregate_sum values, returning two independent analytical reports.","exampleAgentPrompt":"Run string normalisation and numeric distribution analysis 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, source_sum values of 10, 20, 15, 30 and aggregate_sum values of 1, 2, 3, 4 — analyze the source_sum field.","exampleUseCases":[{"title":"Financial reconciliation data quality check","prompt":"I have a batch of 50 aggregation check rows with source_sum and aggregate_sum values across two dataset groups — run string normalisation on the group labels and give me the numeric distribution of the source_sum field so I can spot any anomalies."},{"title":"ETL pipeline output validation","prompt":"After our nightly ETL run I have 120 aggregation records with dates, row IDs, dataset groups, and both source and aggregate sums — can you normalise the string fields and show me the numeric distribution of aggregate_sum to validate the pipeline output?"},{"title":"Monthly reporting data audit","prompt":"I need to audit our monthly aggregation data — 80 rows grouped by region, each with a source_sum and aggregate_sum — please run string normalisation on the group names and compute the numeric distribution of source_sum so I can include it in the audit report."}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report covering the dataset_group and other string fields in the supplied rows, and (2) a numeric distribution report for the selected field (source_sum or aggregate_sum), covering distribution statistics such as min, max, mean, spread, and frequency across up to 200 rows. No external data is fetched; analysis is performed entirely on the supplied records.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, dataset_group, date, source_sum, aggregate_sum) on any row — error returned","Date field not matching YYYY-MM-DD pattern — validation error","Numeric values outside ±1,000,000,000,000 range — rejected","Invalid field enum value (not 'source_sum' or 'aggregate_sum') — error","Payment of 0.01 USDC not completed via x402 protocol — 402 response"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of group/category fields and numeric distribution analysis for aggregation check records in a single low-cost call. It is ideal for data quality workflows involving source-vs-aggregate reconciliation data, ETL validation, or audit reporting where you already have the rows locally and need two analytical outputs without any external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:59.982Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-string-normalisation-numeric-distribution-bb053b8f"}