{"uid":"cap_fwaDbT_OBImxWOitvcwEb","slug":"time-series-completeness-record-quality-audit-daily-sum-series-5e8716fe","name":"Time Series Completeness — Record Quality Audit + Daily Sum Series","description":"Time series completeness — Record quality audit + Daily sum series: Record quality audit + Daily sum series 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-16-009?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_CWGxayuGwbx-tsjUsZSa3","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 (record quality audit and daily sum series) on up to 200 time series completeness records containing observed and expected data points.","exampleAgentPrompt":"Can you run a record quality audit and daily sum series on my time series completeness data? I have rows for dataset groups 'sensors-a' and 'sensors-b' from 2024-01-01 to 2024-01-07, with observed_points and expected_points for each day — analyze the observed_points field.","exampleUseCases":[{"title":"IoT sensor data completeness audit","prompt":"I have a week of IoT sensor data where each row tracks how many readings we expected vs actually received per day per sensor group — can you run a record quality audit and daily sum series on these completeness records to spot gaps or bad data?"},{"title":"ETL pipeline data quality check","prompt":"We run daily ETL jobs and log observed vs expected record counts per pipeline group — please audit the quality of these completeness records and give me a daily sum breakdown for observed_points so I can see where pipelines are underperforming."},{"title":"Multi-dataset completeness monitoring report","prompt":"I'm tracking data completeness across 5 dataset groups over the past month, with each row showing the date, group name, and how many points we observed versus expected — run both a record quality audit and daily sum series on this so I can report on data health."}],"resultDescription":"Returns two analytical reports: (1) a record quality audit highlighting issues, anomalies, or quality flags across the supplied completeness rows, and (2) a daily sum series aggregating the specified field (observed_points or expected_points) by date. Both reports are computed locally with no external lookups or predictions, covering up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error","Invalid date format (must match YYYY-MM-DD) — schema validation failure","Null values in numeric fields may affect sum series calculations","Payment of 0.01 USDC not completed — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need both a record-level quality audit AND a daily aggregated sum series in a single call for time series completeness data (observed vs expected points). It is ideal for batch analysis of up to 200 rows without requiring any external data sources, live lookups, or predictions — purely local computation on your supplied data. Prefer this over single-report endpoints when you want two complementary views (row-level quality + daily totals) for the cost of one call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:07.210Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-daily-sum-series-5e8716fe"}