{"uid":"cap_0oLSbfavtdVPdN0dRviwU","slug":"aggregation-checks-numeric-row-ordering-explicit-missing-value-fill-18af2ed5","name":"Aggregation Checks — Numeric Row Ordering + Explicit Missing-Value Fill","description":"Aggregation checks — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill 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-17-124?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_opRBjAEajt5ImgZdoSog8","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":"Sorts aggregation check rows numerically and fills missing source_sum or aggregate_sum values using zero, mean, or median imputation, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 aggregation check rows with row_id, dataset_group, date, source_sum, and aggregate_sum fields — some source_sum values are null. Can you sort them numerically and fill the missing source_sum values using the median method, then give me two analytical reports?","exampleUseCases":[{"title":"Reconciling financial aggregation gaps","prompt":"I've got a dataset of 150 aggregation check records across two groups — 'actuals' and 'estimates' — with some aggregate_sum values missing. Fill those nulls using the mean method and sort the rows numerically, then give me the two reports."},{"title":"Cleaning sales rollup data before reporting","prompt":"We're doing a sales rollup validation and some of our source_sum entries are null. Can you apply a zero fill to the source_sum field, order the rows numerically by date, and run the aggregation check reports on our 80 records?"},{"title":"Auditing data pipeline output for missing values","prompt":"Our data pipeline produced 200 aggregation rows across dataset groups 'pipeline-a' and 'pipeline-b' and we need to detect ordering issues and fill missing aggregate_sum values using the median — can you run the two analytical reports on this?"}],"resultDescription":"Returns two independent analytical reports covering: (1) numeric row ordering of the supplied aggregation check records by date or row sequence, and (2) explicit missing-value fill applied to the specified field (source_sum or aggregate_sum) using the chosen method (zero, mean, or median). Reports are generated locally without external lookups or predictions.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — request rejected (maxItems limit)","Invalid date format (not YYYY-MM-DD) — schema validation error","source_sum or aggregate_sum values outside ±1,000,000,000,000 range — rejected","Invalid method enum value (not zero/mean/median) — schema validation error","Invalid field enum value (not source_sum/aggregate_sum) — schema validation error","Payment not included or insufficient USDC — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need both numeric row ordering and null imputation on aggregation check data (source_sum + aggregate_sum pairs) in a single $0.01 call that delivers two reports. It is ideal for data reconciliation workflows where missing values must be filled before downstream aggregation validation, and where no live external data lookup or prediction is needed — pure local computation on up to 200 supplied rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:56.229Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-numeric-row-ordering-explicit-missing-value-fill-18af2ed5"}