{"uid":"cap_Il8VOHt82A3t1WgwQQ2kk","slug":"time-series-completeness-record-quality-audit-date-by-category-pivot-d0dd2500","name":"Time Series Completeness — Record Quality Audit + Date by Category Pivot","description":"Time series completeness — Record quality audit + Date by category pivot: Record quality audit + Date by category pivot for supplied time series completeness records (observed_points, expected_points). 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-16-017?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"}}},"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_lBdrSNgt5_0ds1Y8Km-FN","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":"Runs two local analytical reports on time series completeness data: a record quality audit and a date-by-category pivot, for up to 200 rows of observed vs expected points.","exampleAgentPrompt":"Can you run a record quality audit and a date-by-category pivot on my completeness data? I have rows with fields like dataset_group, date, observed_points, and expected_points — about 50 rows covering groups 'sensor-A' and 'sensor-B' across October 2026, and I want to analyze the observed_points field.","exampleUseCases":[{"title":"IoT sensor coverage gap detection","prompt":"I have daily completeness records for two IoT sensor groups across the past month — each row has a date, group name, observed_points, and expected_points. Can you run a record quality audit and a date-by-category pivot so I can see where coverage is falling short?"},{"title":"ETL pipeline completeness review","prompt":"My ETL pipeline produces daily point counts per dataset group. I want to audit the quality of those completeness records and get a pivot of observed_points by date and category — I'll supply the rows with row_id, dataset_group, date, observed_points, and expected_points."},{"title":"Monthly data completeness reporting","prompt":"I need to generate two reports from my monthly completeness dataset: a record quality audit and a date-by-category pivot. The data has about 120 rows across 4 groups with expected and observed point counts per day — can you run both reports on the expected_points field?"}],"resultDescription":"Two analytical reports derived entirely from the supplied data: (1) a record quality audit flagging anomalies, nulls, or integrity issues per row, and (2) a date-by-category pivot summarizing observed or expected points across dataset groups and dates. No external lookups or predictions are made.","failureModes":["Fewer than 1 or more than 200 rows supplied — request rejected","Missing required fields (row_id, date, dataset_group, observed_points, expected_points) — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","observed_points or expected_points outside allowed numeric range — rejection","Invalid field enum value (not 'observed_points' or 'expected_points') — error response"],"whenToPreferThis":"Choose this endpoint when you need two simultaneous local analytical reports — a record quality audit and a date-by-category pivot — on time series completeness data with observed and expected point counts. It is ideal for batch offline analysis of up to 200 rows without requiring any external data sources, predictions, or live lookups. Prefer it over single-report endpoints when both audit and pivot insights are needed in one call at minimal cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:39.844Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-date-by-category-pivot-d0dd2500"}