{"uid":"cap_qL76FbHyzwUPvxFiJ2PkW","slug":"time-series-completeness-string-normalisation-duplicate-key-report-a06daea3","name":"Time Series Completeness — String Normalisation & Duplicate Key Report","description":"Time series completeness — String normalisation + Duplicate key report: String normalisation + Duplicate key report 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-16-021?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_SG18naEmH1DuYVjmTLye6","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":"Analyzes up to 200 time series completeness records to produce two local analytical reports: string normalisation and duplicate key detection for observed/expected data points.","exampleAgentPrompt":"I have a batch of up to 50 time series completeness records with row IDs, dataset groups, dates, and observed vs expected point counts — can you run string normalization and duplicate key detection on them and give me both reports?","exampleUseCases":[{"title":"ETL pipeline data quality check","prompt":"We just ingested 120 rows of daily completeness metrics for our data pipelines — each row has a row_id, dataset_group, date, observed_points, and expected_points. Can you normalize the string fields and flag any duplicate keys across those rows so we can catch data quality issues before loading?"},{"title":"Audit sensor dataset for duplicates","prompt":"I have a time series completeness log for our IoT sensor datasets — about 80 rows covering October 2026, each with an observed and expected point count per group and date. Please run the duplicate key report and string normalization report on all of them."},{"title":"Pre-analysis deduplication of metrics data","prompt":"Before I run my completeness analysis, I want to clean up this batch of 35 completeness records — dataset_group names might have casing inconsistencies and there could be duplicate row IDs. Can you produce the string normalisation and duplicate key report for me?"}],"resultDescription":"Returns two independent analytical reports for the submitted rows: (1) a string normalisation report highlighting inconsistencies or normalizable values in string fields (row_id, dataset_group, date), and (2) a duplicate key report identifying rows sharing the same key combinations. Both reports are computed locally without any external lookups or predictions.","failureModes":["Fewer than 1 row submitted — minItems constraint violated","More than 200 rows submitted — maxItems constraint violated","Date field does not match YYYY-MM-DD pattern — pattern validation failure","row_id missing or empty — minLength:1 constraint violated","observed_points or expected_points outside allowed numeric range","Additional properties included in row objects — rejected by additionalProperties:false"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local data-quality analysis on time series completeness records specifically — no external data, no predictions, just fast string normalization and duplicate key detection on batches up to 200 rows for a flat $0.01 USDC covering both reports. Prefer this over general-purpose data quality tools when your data fits the observed_points/expected_points completeness schema and you want deterministic, privacy-safe local processing.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:15.632Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-duplicate-key-report-a06daea3"}