{"uid":"cap_tEj6vQ1pBfcKpFy9t-q_P","slug":"validation-rule-results-string-normalisation-numeric-row-ordering-d6bfc454","name":"Validation Rule Results — String Normalisation + Numeric Row Ordering","description":"Validation rule results — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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-08-025?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_6YBovbWlCcRNnGzbeFvQb","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":"Normalises string fields and orders rows numerically for up to 200 validation rule result records (passed_rules, total_rules), returning 2 independent analytical reports per call.","exampleAgentPrompt":"Can you normalise the string fields and rank these 50 validation rule result rows by passed_rules? I have records with row_id, dataset_group, date, passed_rules, and total_rules — I need both the string normalisation and numeric ordering reports back.","exampleUseCases":[{"title":"Data quality audit across dataset groups","prompt":"I have 120 validation result rows from last month's pipeline run, each with a row_id, dataset_group, date, passed_rules, and total_rules. Can you normalise the dataset_group strings and rank everything by passed_rules so I can spot which groups are underperforming?"},{"title":"Identifying top-performing rule sets","prompt":"Here are 80 validation records from our ETL checks — can you sort them numerically by total_rules and also normalise the dataset_group and row_id strings? I want both reports so I can compare rule coverage across groups."},{"title":"Pre-reporting data cleanup for compliance","prompt":"Before I hand these 200 data-quality records to the compliance team, can you clean up the string fields and order the rows by passed_rules numerically? The records have row_id, dataset_group, date, passed_rules, and total_rules columns."}],"resultDescription":"Two independent analytical reports per call: (1) a string normalisation report for the supplied dataset_group and row_id fields across all submitted rows, and (2) a numeric row ordering report ranking rows by the selected field (passed_rules or total_rules). Up to 200 rows processed per request with no external lookups or live data.","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 validation error","Numeric values outside allowed range (±1,000,000,000,000) — validation error","Invalid date format (must be YYYY-MM-DD) — pattern mismatch error","Empty rows array — minItems violation","Unknown field enum value — rejected with enum constraint error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 validation rule result records and need both string normalisation of categorical fields and numeric ordering of passed_rules or total_rules in a single low-cost call. It is ideal for lightweight data quality pipelines where no live lookups, predictions, or external integrations are required — just deterministic local analytical processing of supplied data at $0.01 USDC for two reports.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T12:48:42.738Z","isFirstParty":false,"canonicalSlug":"validation-rule-results-string-normalisation-numeric-row-ordering-d6bfc454"}