{"uid":"cap_mXi1-hcbQw6lFQfrQ13pJ","slug":"null-coverage-analytics-numeric-interval-filter-adjacent-row-changes-9ae952e1","name":"Null Coverage Analytics: Numeric Interval Filter + Adjacent Row Changes","description":"Null coverage — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes 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-03-127?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_qoEzQCx-akIzz2hBnICH2","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 null coverage records by applying a numeric interval filter and detecting adjacent row changes across up to 200 supplied rows, returning two independent analytical reports.","exampleAgentPrompt":"I have null coverage data for my datasets — can you run two reports on it: filter rows where missing_values falls between 5 and 50, and also flag any adjacent row changes in that field? I'll send up to 50 rows with row_id, date, dataset_group, missing_values, and total_values.","exampleUseCases":[{"title":"Data quality audit interval check","prompt":"I've got a CSV of null coverage records with missing_values and total_values columns — can you filter to just the rows where missing_values is between 0 and 25, and also show me where adjacent rows changed? I have about 80 rows across two dataset groups."},{"title":"Detecting sudden null spikes in pipeline","prompt":"Can you look at my pipeline's null coverage log and flag any consecutive rows where total_values jumped or dropped, and also filter to rows where total_values is between 100 and 500? I'm passing in around 120 rows with dates and group labels."},{"title":"Missing value threshold monitoring","prompt":"Run a quick analysis on my dataset's null coverage: filter missing_values between 10 and 200, and tell me where adjacent row values changed significantly — I have 40 rows from group-a and group-b across October 2025."}],"resultDescription":"Returns two independent analytical reports on the supplied null coverage data: (1) rows where the selected field (missing_values or total_values) falls within the specified numeric interval [minimum, maximum], and (2) a report of adjacent row changes in that field across sequential records — all computed locally without any external lookups.","failureModes":["Missing required fields (row_id, date, missing_values, total_values) causes validation error","Supplying more than 200 rows exceeds the row limit","Date values not matching YYYY-MM-DD pattern are rejected","Numeric values outside ±1,000,000,000,000 bounds are invalid","Empty rows array returns error","minimum greater than maximum produces empty interval filter results"],"whenToPreferThis":"Choose this endpoint when you have up to 200 pre-collected null coverage records and need lightweight, offline analytics — specifically a numeric interval filter combined with adjacent row change detection — without needing live data fetching, predictions, or external integrations. Ideal for data quality pipelines, ETL monitoring, or profiling dataset completeness over time.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:08.376Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-numeric-interval-filter-adjacent-row-changes-9ae952e1"}