{"uid":"cap_6ecbDpOeGpGQR4KTRnJ46","slug":"time-series-completeness-record-quality-audit-numeric-distribution-20fee37f","name":"Time Series Completeness — Record Quality Audit & Numeric Distribution","description":"Time series completeness — Record quality audit + Numeric distribution: Record quality audit + Numeric distribution 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-16-004?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_f9iFEqX0x62rCErkG2RMS","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 analytical reports (record quality audit and numeric distribution) on time series completeness data (observed_points vs expected_points) for up to 200 rows, using local analytical engines with no external lookups.","exampleAgentPrompt":"Can you run a record quality audit and numeric distribution report on my time series completeness data? I have up to 50 rows with columns row_id, dataset_group, date, observed_points, and expected_points — analyze the observed_points field.","exampleUseCases":[{"title":"IoT sensor gap detection audit","prompt":"I have daily completeness records for my IoT sensor network — each row has a date, a dataset_group like 'sensor-cluster-a', and observed_points vs expected_points. Can you run a record quality audit and numeric distribution report on the observed_points to see where data is missing or irregular?"},{"title":"Data pipeline completeness review","prompt":"Our ETL pipeline tracks expected vs observed data points per day across several pipeline groups. I have about 120 rows. Can you audit the record quality and give me a numeric distribution breakdown of expected_points so I know where our pipelines are underperforming?"},{"title":"Time series QA before model training","prompt":"Before I feed this time series completeness dataset into our ML model, I want to check it for quality issues and understand the distribution of observed_points. Can you run both the record quality audit and numeric distribution report on these 80 rows?"}],"resultDescription":"Two structured analytical reports: (1) a record quality audit highlighting issues such as nulls, malformed entries, or anomalies in the completeness records; and (2) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram) for the selected field (observed_points or expected_points). No external data is fetched — all analysis is performed locally on the supplied rows.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Missing required fields (row_id, date, observed_points, expected_points) in any row — validation error","Date strings not matching YYYY-MM-DD pattern — schema validation failure","Field selector not one of 'observed_points' or 'expected_points' — enum validation error","All numeric values are null — distribution report may return empty or degenerate results","Payment not received or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have tabular time series completeness data (observed vs expected point counts) and need both a record quality audit and a numeric distribution profile in a single call for up to 200 rows. It is ideal for data engineers, analysts, or automated pipelines that need lightweight, local statistical profiling without any external lookups or predictions. Prefer it over general-purpose analytics tools when you want a cost-efficient ($0.01 USDC), focused dual-report output specifically scoped to completeness metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:34:20.143Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-numeric-distribution-20fee37f"}