{"uid":"cap__V0npILcoYGEvNA8sJLCD","slug":"time-series-completeness-record-quality-audit-string-normalisation-2585f1aa","name":"Time Series Completeness — Record Quality Audit + String Normalisation","description":"Time series completeness — Record quality audit + String normalisation: Record quality audit + String normalisation 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-16-001?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}}},"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_zGVfnaKsMbDqvxLbJHwfE","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":"Audits record quality and normalises strings for supplied time series completeness data (observed_points, expected_points) across up to 200 rows, producing 2 independent analytical reports.","exampleAgentPrompt":"Can you run a record quality audit and string normalisation on my time series completeness data? I have rows for group-a and group-b from 2026-10-01 to 2026-10-04, with observed_points values like 10, 20, 15, 30 and expected_points like 1, 2, 3, 4 — give me both analytical reports.","exampleUseCases":[{"title":"ETL pipeline completeness check","prompt":"I have daily completeness records for two dataset groups — 'sensor-east' and 'sensor-west' — covering the past 5 days with observed and expected point counts. Can you run a record quality audit and string normalisation on all 10 rows and give me the two reports?"},{"title":"Data warehouse coverage audit","prompt":"Audit the quality of these 50 time series completeness records for our data warehouse, split across groups 'dw-core' and 'dw-archive', with dates from 2025-01-01 to 2025-02-19 and their observed vs expected point counts — I need both the record quality and string normalisation reports."},{"title":"Monitoring dashboard data validation","prompt":"Before I load these completeness metrics into our monitoring dashboard, can you run a quality audit and string normalisation check on these 30 rows? They cover groups 'api-calls' and 'batch-jobs' with daily observed_points and expected_points for March 2025."}],"resultDescription":"Two independent analytical reports: (1) a record quality audit flagging issues such as missing values, out-of-range numbers, or malformed dates in the completeness rows, and (2) a string normalisation report covering dataset_group and row_id field formatting. Both reports cover up to 200 supplied rows and are generated using local analytical engines with no external lookups.","failureModes":["Submitting more than 200 rows returns a validation error","Rows missing required fields (row_id, date, observed_points, expected_points) are rejected or flagged","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Null or out-of-range numeric values may produce incomplete audit results","Malformed JSON body returns a 400-level error","Payment failure via x402 prevents processing"],"whenToPreferThis":"Choose this endpoint when you have raw time series completeness records (observed vs expected point counts) and need both a structured data quality audit and string normalisation without any live data lookups, external predictions, or professional assessments. Ideal for lightweight, low-cost ($0.01 USDC) batch validation of up to 200 rows within an automated data pipeline or ETL workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:19.275Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-string-normalisation-2585f1aa"}