{"uid":"cap_IQLfGqZeh0FDttnyboPa3","slug":"validation-rule-results-numeric-distribution-missing-value-fill-c8c24d1a","name":"Validation Rule Results — Numeric Distribution & Missing-Value Fill","description":"Validation rule results — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill 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-08-085?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_R241JqPlLMNMnnU-sio1R","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 passed_rules and total_rules fields from validation rule result records, producing a numeric distribution report and an explicit missing-value fill report for up to 200 rows.","exampleAgentPrompt":"Can you run a numeric distribution report and fill in any missing passed_rules values using median imputation on my validation results? I have about 50 rows with row IDs, dataset groups, dates, and passed_rules and total_rules counts.","exampleUseCases":[{"title":"Audit rule pass rates across dataset groups","prompt":"I have 120 rows of validation results with passed_rules and total_rules for several dataset groups. Can you give me a numeric distribution of passed_rules and fill any nulls using the mean so I can see how consistently rules are passing?"},{"title":"Impute missing total_rules before pipeline export","prompt":"Before I export my validation results, I need any missing total_rules values filled in using zero-fill. Can you run the missing-value fill report on my 80 rows and also show me the numeric distribution of total_rules?"},{"title":"QA check on data quality rule outcomes","prompt":"I'm doing a QA review — can you analyze the numeric distribution of passed_rules for my 35 validation records and fill any missing entries using median so the numbers are consistent across all rows?"}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution summary of the selected field (passed_rules or total_rules) across up to 200 rows, and (2) an explicit missing-value fill report where null/missing values in the selected field are replaced using the chosen method (zero, mean, or median). No external lookups or live data involved.","failureModes":["Exceeds 200-row limit — request rejected","Invalid date format (not YYYY-MM-DD) — validation error","Field value outside allowed numeric range — rejected","Missing required fields (row_id, dataset_group, date) — schema error","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Use this endpoint when you need a quick, low-cost combined report (numeric distribution + missing-value imputation) on validation rule result data (passed_rules, total_rules) without needing external lookups or ML-based predictions. Ideal for data pipeline QA steps, pre-export audits, or data quality monitoring workflows where you want statistical summaries and imputed nulls in a single $0.01 call covering up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-14T00:37:19.984Z","isFirstParty":false,"canonicalSlug":"validation-rule-results-numeric-distribution-missing-value-fill-c8c24d1a"}