{"uid":"cap_-donHiefXpvNm1cqHKzhm","slug":"validation-rule-results-string-normalisation-category-frequencies-4a0222b9","name":"Validation Rule Results — String Normalisation + Category Frequencies","description":"Validation rule results — String normalisation + Category frequencies: String normalisation + Category frequencies 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-08-024?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}}},"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_8L7nhiMCL67kP6Ar06lKY","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":"Analyzes up to 200 validation rule result records (passed_rules, total_rules) and produces two reports: string normalization and category frequency distributions, using local analytical engines with no external lookups.","exampleAgentPrompt":"Can you run string normalization and category frequency analysis on my validation rule results? I have 4 rows — row-1 on 2026-10-01 in group-a with 10 passed out of 1 total, row-2 on 2026-10-02 in group-b with 20 passed out of 2 total, row-3 on 2026-10-03 in group-a with 15 passed out of 3 total, and row-4 on 2026-10-04 in group-b with 30 passed out of 4 total.","exampleUseCases":[{"title":"Audit ETL pipeline validation compliance","prompt":"I've got 50 rows of validation rule results from our ETL pipeline — each row has a dataset group, a date, and passed_rules vs total_rules counts. Can you normalize the string fields and give me a category frequency breakdown by dataset group?"},{"title":"Data quality dashboard by group","prompt":"We track rule pass rates across three dataset groups daily. Can you take these 120 validation records with their passed_rules and total_rules values and produce a string normalization report plus a category frequency summary so I can see which groups are failing most?"},{"title":"Pre-release data validation review","prompt":"Before we ship this dataset, I want to double-check our validation results. Here are 80 rows covering the last two weeks — each with a row_id, date, dataset_group, passed_rules, and total_rules. Run string normalization and category frequencies on them so I can spot any anomalies."}],"resultDescription":"Two analytical reports: (1) a string normalization report that standardizes and cleans string fields (such as dataset_group) across the submitted rows, and (2) a category frequency report showing the distribution and count of values across categorical dimensions like dataset_group and date, all computed locally without any external data lookups.","failureModes":["More than 200 rows submitted — exceeds maxItems limit and will be rejected","Missing required fields (row_id, dataset_group, date, passed_rules, total_rules) causes validation error","Date field not matching pattern YYYY-MM-DD causes schema rejection","passed_rules or total_rules values outside the allowed numeric range cause validation failure","Empty rows array (minItems: 1 not met) returns an error"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous analytical reports — string normalization and category frequency distributions — on validation rule result records (passed_rules, total_rules) without any need for external lookups, predictions, or live data. It is especially useful for data quality workflows, ETL auditing, and compliance reporting where you want fast, local computation on batches up to 200 rows at a low fixed cost of $0.01 USDC per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T12:48:35.564Z","isFirstParty":false,"canonicalSlug":"validation-rule-results-string-normalisation-category-frequencies-4a0222b9"}