{"uid":"cap_I3MT7fpuKhSJRll60rp0S","slug":"time-series-completeness-record-quality-audit-string-normalisation-79c5652c","name":"Time Series Completeness — Record Quality Audit + String Normalisation + Duplicate Key Report","description":"Time series completeness — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-192?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_sPv_F_VY4N4dD5ZFOWV9N","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 up to 200 time series completeness records for data quality issues, normalises strings, and identifies duplicate keys across three independent analytical reports.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and duplicate key check on these time series completeness records? I have rows with row_id, dataset_group, date, observed_points, and expected_points — here are 10 rows for group-a and group-b spanning October 2026.","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have 50 rows of time series completeness data from my ETL pipeline — each row has a row_id, dataset_group, date, observed_points, and expected_points. Can you run a full quality audit, normalise the string fields, and flag any duplicate keys?"},{"title":"Detecting duplicate keys before merge","prompt":"Before I merge these time series completeness records into our warehouse, can you run a duplicate key report on the 120 rows I'm supplying? Each row has a row_id, dataset_group like 'sensor-west', a date in YYYY-MM-DD format, and observed and expected point counts."},{"title":"String normalisation for group labels","prompt":"My dataset has inconsistent capitalisation and formatting in the dataset_group field across 80 time series completeness rows. Can you normalise those strings and also give me a record quality audit and duplicate report while you're at it?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit highlighting issues in the supplied time series completeness rows, (2) a string normalisation report showing standardised versions of string fields like dataset_group, and (3) a duplicate key report identifying rows with repeated row_id values. All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, observed_points, expected_points) cause row-level failures","Invalid date format (non-YYYY-MM-DD) triggers pattern validation error","observed_points or expected_points outside ±1,000,000,000,000 range rejected","Empty rows array returns an error"],"whenToPreferThis":"Choose this endpoint when you need a combined, cost-effective batch audit of time series completeness records covering data quality, string normalisation, and duplicate detection in a single call. Best suited for datasets of up to 200 rows where no live data lookup, prediction, or external enrichment is needed — just local analytical processing of supplied observed_points and expected_points data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:44.223Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-string-normalisation-79c5652c"}