{"uid":"cap_2D_EPLh497kRXxxhDUg_C","slug":"validation-rule-results-string-normalisation-date-by-category-pivot-502f80ae","name":"Validation Rule Results — String Normalisation + Date by Category Pivot","description":"Validation rule results — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied validation rule results records (passed_rules, total_rules). 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-08-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","passed_rules","total_rules"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_rules":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"passed_rules":{"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","total_rules":1,"passed_rules":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","total_rules":2,"passed_rules":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","total_rules":3,"passed_rules":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","total_rules":4,"passed_rules":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["passed_rules","total_rules"],"type":"string","default":"passed_rules"}}},"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_87I5TyS4Xl5kunJCZ4KUW","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":"Applies string normalisation and a date-by-category pivot analysis to up to 200 validation rule result records (passed_rules, total_rules), returning two independent analytical reports.","exampleAgentPrompt":"I have up to 200 rows of validation rule results with fields like row_id, dataset_group, date, passed_rules, and total_rules — can you run string normalisation and a date-by-category pivot on the passed_rules field and give me both reports?","exampleUseCases":[{"title":"Daily data quality compliance pivot","prompt":"I have 150 rows of validation results from our ETL pipeline across two dataset groups over the past month — can you pivot the passed_rules counts by date and group, and normalise the string fields at the same time?"},{"title":"Rule coverage audit across dataset groups","prompt":"Can you run string normalisation and a date-by-category pivot on these 80 validation rule records using total_rules as the field, so I can see how rule coverage varies by dataset_group over time?"},{"title":"Pre-reporting validation metric cleanup","prompt":"Before I generate our weekly quality report, can you normalise the string fields and produce a date-by-category pivot for my 200 validation rule result rows based on passed_rules?"}],"resultDescription":"Two independent analytical reports: (1) a string-normalised view of the input validation rule result records, and (2) a date-by-category pivot table aggregating the selected numeric field (passed_rules or total_rules) by date and dataset_group, covering up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected with validation error","Missing required fields (row_id, dataset_group, date, passed_rules, total_rules) — row-level or request-level error","Invalid date format (not YYYY-MM-DD) — pattern validation failure","Numeric field values outside allowed range — validation error","Empty rows array — minimum items constraint failure"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and a date-by-category pivot on validation rule result data (passed_rules / total_rules) in a single call for up to 200 rows. It is well-suited for data quality reporting pipelines where you want two complementary views — cleaned string data and a time-group breakdown — without external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T12:48:41.667Z","isFirstParty":false,"canonicalSlug":"validation-rule-results-string-normalisation-date-by-category-pivot-502f80ae"}