{"uid":"cap_dydQ-ltcGkkyJ0vvYytgy","slug":"null-coverage-trailing-mean-series-date-by-category-pivot-0dbb9c89","name":"Null Coverage — Trailing Mean Series + Date by Category Pivot","description":"Null coverage — Trailing mean series + Date by category pivot: Trailing mean series + Date by category pivot 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-03-152?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"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_c6AXNTFijmdE-bna59Te6","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":"Computes a trailing moving-average time series and a date-by-category pivot table for null coverage data (missing_values, total_values) supplied by the caller, up to 200 rows.","exampleAgentPrompt":"Run a trailing mean series and date-by-category pivot on my null coverage data — I have 4 rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, missing_values ranging 10–30 and total_values 1–4. Use the missing_values field with a window size of 2.","exampleUseCases":[{"title":"Data pipeline null rate monitoring","prompt":"I have daily records for two pipeline groups — 'ingest' and 'transform' — with missing_values and total_values logged per day across 30 days. Can you run a trailing mean series with a window of 7 on missing_values and also give me a date-by-category pivot so I can see which group is drifting?"},{"title":"Data quality audit for multiple datasets","prompt":"I'm auditing data completeness across 5 dataset groups over 3 months — about 90 rows total. Run a rolling mean with window size 5 on total_values and produce a pivot of dates vs categories so I can spot which groups have the worst coverage over time."},{"title":"Weekly missing-value trend report","prompt":"For my weekly data quality report, take these 52 rows of null coverage records (one per week, two groups) and compute a trailing mean series on missing_values using a window of 4, plus a date-by-category pivot so I can see the breakdown at a glance."}],"resultDescription":"Returns two analytical reports: (1) a trailing moving-average time series for the selected field (missing_values or total_values) with the specified window size, and (2) a pivot table of values by date and dataset_group category — all computed locally from the supplied rows with no external lookups.","failureModes":["Fewer than 1 row or more than 200 rows supplied — request rejected","Window size smaller than 2 or larger than 200 — validation error","Missing required fields (row_id, date, dataset_group) on any row — error returned","Invalid date format (must match YYYY-MM-DD) — schema validation failure","Null numeric values for missing_values or total_values may affect mean calculation accuracy","Payment of 0.01 USDC not completed — endpoint returns 402"],"whenToPreferThis":"Choose this endpoint when you need both a rolling/trailing mean time series AND a date-by-category pivot computed together from user-supplied null coverage records in a single $0.01 call. Ideal for data quality monitoring workflows where you want to track missing value trends over time across multiple dataset groups without any external data lookup or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:10.495Z","isFirstParty":false,"canonicalSlug":"null-coverage-trailing-mean-series-date-by-category-pivot-0dbb9c89"}