{"uid":"cap_FHXLK3AqpgQilQm2YuT03","slug":"data-validation-results-adjacent-row-changes-116020b7","name":"Data Validation Results – Adjacent Row Changes","description":"Data validation results - Adjacent row changes: Adjacent row changes for Data validation results. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded adjacent row changes report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-validation-results-period-change?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","rule","date","checked_records","failed_records"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"rule":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"failed_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"checked_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","rule":"group-a","row_id":"row-1","failed_records":1,"checked_records":10},{"date":"2026-10-02","rule":"group-b","row_id":"row-2","failed_records":2,"checked_records":20},{"date":"2026-10-03","rule":"group-a","row_id":"row-3","failed_records":3,"checked_records":15},{"date":"2026-10-04","rule":"group-b","row_id":"row-4","failed_records":4,"checked_records":30}],"maxItems":200,"minItems":1},"field":{"enum":["checked_records","failed_records"],"type":"string","default":"checked_records"}}},"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_JGgp1NhNjpWo7x7dMIyvK","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 adjacent row-over-row changes in checked or failed record counts for data validation results, returning local descriptive analytics for up to 200 typed records.","exampleAgentPrompt":"Can you compute the adjacent row-over-row changes in failed_records for my validation results? I have about 50 rows, each with a row_id, rule name, date, checked_records, and failed_records — I want to see how the failure counts shifted between consecutive rows.","exampleUseCases":[{"title":"Daily pipeline data quality monitoring","prompt":"I've got a week's worth of daily validation results for my ETL pipeline — each row has a date, rule name, checked records, and failed records. Can you run the adjacent row changes report on failed_records so I can see where failure counts spiked or dropped between consecutive days?"},{"title":"Audit report on validation rule drift","prompt":"We ran our data validation suite across 30 dates and I need to show auditors how checked_records shifted between each adjacent period for rule 'null-check-primary'. Can you compute the row-over-row changes for the checked_records field across my validation results?"},{"title":"Spotting anomalies in data quality trends","prompt":"Here are 80 rows of validation results from our data warehouse checks — each has a date, a rule label, and counts of checked and failed records. Run the adjacent row changes analysis on failed_records so I can spot any sudden anomalies between consecutive reporting periods."}],"resultDescription":"Returns a bounded adjacent row changes report showing period-over-period deltas between consecutive rows for the selected field (checked_records or failed_records), including row identifiers, rule names, dates, and computed change values — purely local descriptive analytics with no external enrichment or forecasting.","failureModes":["Fewer than 1 row submitted returns a validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Missing required fields (row_id, rule, date, checked_records, failed_records) results in a 400 error","Non-numeric values for checked_records or failed_records outside allowed range are rejected","Invalid field enum value (not checked_records or failed_records) causes a bad request error"],"whenToPreferThis":"Choose this endpoint when you have sequential data validation result rows and need a lightweight, local period-over-period delta analysis on either checked or failed record counts — without requiring any external data enrichment or forecasting. It is ideal for ETL monitoring dashboards, data quality audits, or anomaly detection workflows where you want row-adjacent change summaries for up to 200 records at very low cost ($0.01 USDC per call).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:24.804Z","isFirstParty":false,"canonicalSlug":"data-validation-results-adjacent-row-changes-116020b7"}