{"uid":"cap_3A6PNElBJBHJ5GuJjTcHl","slug":"null-coverage-analytics-adjacent-row-changes-iqr-outlier-flags-dc484c0c","name":"Null Coverage Analytics — Adjacent Row Changes & IQR Outlier Flags","description":"Null coverage — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags for supplied null coverage records (missing_values, total_values). 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-03-157?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","missing_values","total_values"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"missing_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_values":1,"dataset_group":"group-a","missing_values":10},{"date":"2026-10-02","row_id":"row-2","total_values":2,"dataset_group":"group-b","missing_values":20},{"date":"2026-10-03","row_id":"row-3","total_values":3,"dataset_group":"group-a","missing_values":15},{"date":"2026-10-04","row_id":"row-4","total_values":4,"dataset_group":"group-b","missing_values":30}],"maxItems":200,"minItems":1},"field":{"enum":["missing_values","total_values"],"type":"string","default":"missing_values"}}},"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_wY4WdqHM_pvY3JMYMMLDi","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 supplied null coverage records (missing_values, total_values) to produce two reports: adjacent row change detection and IQR-based outlier flagging, for up to 200 rows.","exampleAgentPrompt":"I have null coverage records for my datasets — can you run both an adjacent-row change analysis and an IQR outlier check on the missing_values field for these rows: [{\"row_id\":\"r1\",\"dataset_group\":\"sales\",\"date\":\"2024-01-01\",\"missing_values\":5,\"total_values\":100},{\"row_id\":\"r2\",\"dataset_group\":\"sales\",\"date\":\"2024-01-02\",\"missing_values\":80,\"total_values\":100},{\"row_id\":\"r3\",\"dataset_group\":\"sales\",\"date\":\"2024-01-03\",\"missing_values\":6,\"total_values\":100}]?","exampleUseCases":[{"title":"Spike detection in null rates","prompt":"I have daily null coverage records for my 'inventory' dataset group — can you check the missing_values field for sudden jumps between adjacent rows and flag any IQR outliers? Here are 10 rows spanning October 2024."},{"title":"Data quality audit across groups","prompt":"Run adjacent row change and IQR outlier analysis on total_values across these 50 null coverage records from my pipeline — I need to know which rows look anomalous before I publish this report."},{"title":"Monitoring completeness drift","prompt":"I want to catch any unusual drifts in missing_values across my dataset groups — can you analyze these 120 rows of null coverage data for both row-to-row changes and statistical outliers using IQR?"}],"resultDescription":"Two independent analytical reports per call: (1) adjacent row change report showing how missing_values or total_values shift between consecutive rows, and (2) IQR outlier flags identifying rows whose values fall outside the interquartile range boundary, all computed locally on the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — minimum row count not met","More than 200 rows submitted — exceeds maximum batch size","Missing required fields (row_id, date, missing_values, total_values) on any row — validation error","Invalid date format (not YYYY-MM-DD) causes row rejection","Null or non-numeric values in both missing_values and total_values may reduce report accuracy","Unknown field enum value (not 'missing_values' or 'total_values') causes request failure"],"whenToPreferThis":"Choose this endpoint when you need both adjacent-row change detection AND IQR outlier flagging on null coverage data in a single call, without requiring any external data source or live lookup. It is ideal for data quality pipelines that batch-process completeness metrics, audit missing value patterns, or need statistical anomaly flags on small-to-medium datasets (up to 200 rows) at very low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:01.403Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-adjacent-row-changes-iqr-outlier-flags-dc484c0c"}