{"uid":"cap_B2Pc_fLvdnqnD_9AifUvM","slug":"data-ingestion-batches-record-quality-audit-adjacent-row-changes-cd0b4abc","name":"Data Ingestion Batches — Record Quality Audit & Adjacent Row Changes","description":"Data ingestion batches — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes for supplied data ingestion batches records (received_rows, accepted_rows). 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-01-011?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","received_rows","accepted_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"accepted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"received_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","accepted_rows":1,"dataset_group":"group-a","received_rows":10},{"date":"2026-10-02","row_id":"row-2","accepted_rows":2,"dataset_group":"group-b","received_rows":20},{"date":"2026-10-03","row_id":"row-3","accepted_rows":3,"dataset_group":"group-a","received_rows":15},{"date":"2026-10-04","row_id":"row-4","accepted_rows":4,"dataset_group":"group-b","received_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["received_rows","accepted_rows"],"type":"string","default":"received_rows"}}},"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_CK3YMBPKJzldvf8yitDrv","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":"Performs a two-report analysis on up to 200 data ingestion batch records: a record quality audit and adjacent row change detection for received_rows or accepted_rows fields.","exampleAgentPrompt":"Can you run a quality audit and adjacent row change analysis on my ingestion batch data? Here are 4 records across two dataset groups — group-a and group-b — from 2026-10-01 to 2026-10-04, with received_rows and accepted_rows values, and I want to analyze the accepted_rows field.","exampleUseCases":[{"title":"Pipeline data quality review","prompt":"I have a week's worth of ingestion batch records for two dataset groups — 'customers' and 'transactions' — with received and accepted row counts per day. Can you run a quality audit and flag any records that look off, and also show me how accepted_rows changed between consecutive records?"},{"title":"Acceptance rate anomaly detection","prompt":"My data pipeline has been behaving strangely. I have 50 daily batch records for the 'orders' dataset group with received_rows and accepted_rows values. Can you audit the record quality and show me the adjacent row changes for accepted_rows so I can spot where something went wrong?"},{"title":"Cross-group ingestion comparison","prompt":"I'm reviewing ingestion health across three dataset groups — 'logs', 'events', and 'metrics' — and I have about 120 batch records spanning last month. Please run a quality audit and adjacent row change report on the received_rows field so I can see which groups have inconsistencies."}],"resultDescription":"Returns two independent analytical reports: (1) a record quality audit flagging issues or anomalies in the supplied ingestion batch records, and (2) an adjacent row change report showing differences and eligible percentage changes between consecutive rows — both computed for the specified field (received_rows or accepted_rows) across up to 200 records.","failureModes":["More than 200 rows submitted — request will be rejected or truncated","Missing required fields (row_id, dataset_group, date, received_rows, accepted_rows) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","Null values in the target field may affect adjacency change calculations","row_id exceeds 80-character limit — validation error","Empty rows array — minItems constraint violation"],"whenToPreferThis":"Choose this endpoint when you have tabular ingestion batch data with received_rows and accepted_rows metrics and need both a data quality audit and a period-over-period row change analysis in a single call. It is best suited for small-to-medium batch sizes (up to 200 rows) where you want two complementary analytical perspectives — quality flags and trend changes — without any external lookups or live data fetching. Prefer this over generic analytics tools when your data is already structured with dataset_group and date fields and you need fast, local computation results for under $0.01.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-14T00:37:28.445Z","isFirstParty":false,"canonicalSlug":"data-ingestion-batches-record-quality-audit-adjacent-row-changes-cd0b4abc"}