{"uid":"cap_hFSpW9IXaoUDBuCGBPtS3","slug":"time-series-completeness-record-quality-audit-string-normalisation-df9f14c7","name":"Time Series Completeness — Record Quality Audit, String Normalisation & Exact Row Deduplication","description":"Time series completeness — Record quality audit + String normalisation + Exact row deduplication: Record quality audit + String normalisation + Exact row deduplication 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-16-191?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_LLaJ8TE32ICyn2Wq3rejq","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 time series completeness records (observed_points vs expected_points) for quality issues, normalises strings, and identifies exact duplicate rows across up to 200 rows, returning 3 independent analytical reports.","exampleAgentPrompt":"Can you audit these time series completeness records for quality issues, normalise the strings, and find exact duplicate rows? I have 4 rows with fields like row_id, dataset_group, date, observed_points, and expected_points — for example row-1 in group-a on 2026-10-01 with 10 observed out of 1 expected.","exampleUseCases":[{"title":"Daily completeness check for ETL pipelines","prompt":"I have 50 rows of daily ETL completeness logs with observed_points and expected_points per dataset_group and date — can you run a record quality audit, normalise any messy strings, and flag exact duplicate rows so I can trust the data going into my dashboard?"},{"title":"Deduplicating multi-source sensor data","prompt":"We're merging completeness records from two sensor feeds and suspect there are exact duplicate rows. Can you audit the data quality, normalise the group names, and give me a deduplication report for these 120 rows with observed and expected point counts per day?"},{"title":"Validating completeness metrics before reporting","prompt":"Before I send this time series completeness report to the client, can you run a quality audit on these 80 rows — checking observed_points vs expected_points across our dataset groups — and also normalise the strings and remove any exact duplicate records?"}],"resultDescription":"Three independent analytical reports delivered together: (1) a record quality audit flagging issues in the time series completeness data, (2) a string normalisation report showing standardised field values, and (3) an exact row deduplication report identifying identical rows — all computed locally with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, observed_points, expected_points) causes rejection","Invalid date format (not YYYY-MM-DD) triggers a schema error","Values outside the allowed numeric range (-1,000,000,000,000 to 1,000,000,000,000) are rejected","Empty rows array is not accepted"],"whenToPreferThis":"Choose this endpoint when you need a bundled, cost-effective ($0.01 USDC for all 3 reports) local analysis of time series completeness records specifically tracking observed vs expected data points — especially when you want quality audit, string normalisation, and exact deduplication in one call without any live lookups or external integrations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:03.087Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-string-normalisation-df9f14c7"}