{"uid":"cap_gBz1qYLTSOwTSpFK15n5m","slug":"aggregation-checks-trailing-mean-series-date-by-category-pivot-1d98ef04","name":"Aggregation Checks — Trailing Mean Series + Date by Category Pivot","description":"Aggregation checks — Trailing mean series + Date by category pivot: Trailing mean series + 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-17-152?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"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_wnhte2ON977gFCHdv8mW6","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":"Computes a trailing moving-average time series and a date-by-category pivot table for aggregation check records containing source_sum and aggregate_sum fields, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Run the trailing mean series and date-by-category pivot on my aggregation check data — here are 4 rows with source_sum and aggregate_sum values across group-a and group-b from 2026-10-01 to 2026-10-04, use a window of 2, and analyze the source_sum field.","exampleUseCases":[{"title":"Reconciliation trend check for finance team","prompt":"I have 150 rows of daily reconciliation records with source_sum and aggregate_sum across three dataset groups from Q3. Can you run the trailing mean series with a window of 7 and a date-by-category pivot on source_sum so I can spot where the aggregations diverge?"},{"title":"Weekly ETL pipeline aggregation audit","prompt":"After each ETL run I need to validate my aggregation sums — I have 80 rows tagged by dataset_group and date. Run a trailing mean series with window 5 and a date pivot on aggregate_sum so I can see group-level patterns over time."},{"title":"Data quality review for monthly reporting","prompt":"I'm preparing a monthly data quality report and need two things: a rolling mean trend for source_sum with a 3-row window, and a date-by-category pivot grouping my 60 aggregation check records by dataset_group. Can you produce both?"}],"resultDescription":"Returns two independent analytical reports: (1) a trailing moving-average time series for the selected field (source_sum or aggregate_sum) computed over the specified window size, and (2) a date-by-category pivot table breaking down values by dataset_group across dates — both derived solely from the supplied input rows (up to 200).","failureModes":["Fewer than 1 row supplied — request rejected by minItems constraint","Window size larger than number of rows — trailing mean series may be empty or partial","Invalid date format (not YYYY-MM-DD) — schema validation error","source_sum or aggregate_sum values outside ±1,000,000,000,000 — rejected by schema bounds","row_id or dataset_group exceeding 80 characters — schema validation error","More than 200 rows supplied — rejected by maxItems constraint"],"whenToPreferThis":"Choose this endpoint when you need both a rolling/trailing mean time series AND a date-by-category pivot table computed together on aggregation check records (source_sum, aggregate_sum) in a single paid call. It is purpose-built for reconciliation and aggregation validation workflows where you supply your own data and want offline analytical summaries — not for live lookups, predictions, or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:31.558Z","isFirstParty":false,"canonicalSlug":"aggregation-checks-trailing-mean-series-date-by-category-pivot-1d98ef04"}