{"uid":"cap_-wBfNzgSRLsK_VoJk5JVK","slug":"time-series-completeness-string-normalisation-numeric-row-ordering-6de5fd22","name":"Time Series Completeness — String Normalisation + Numeric Row Ordering","description":"Time series completeness — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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-16-025?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_6Hc3OAozWak1N8_k_1yXs","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, applying string normalisation and numeric row ordering to produce two independent analytical reports on observed vs expected data points.","exampleAgentPrompt":"I have a batch of time series completeness records — up to 50 rows with dataset groups like 'sensor-a' and 'sensor-b', dates in YYYY-MM-DD format, and both observed_points and expected_points values — can you run string normalisation and numeric row ordering on the observed_points field and give me the two analytical reports?","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I need to audit my data pipeline logs — I have 120 rows with dates, dataset groups, and observed vs expected point counts. Can you normalize the strings and order them numerically on the observed_points field so I can see where gaps are?"},{"title":"Sensor coverage gap detection","prompt":"Here are my sensor readings for October — each row has a date, a sensor group name, and the observed versus expected data points for that day. Please run string normalisation and numeric ordering on expected_points and give me both reports for the 80 rows I'm sending."},{"title":"Daily reporting completeness check","prompt":"I'm preparing a daily completeness report for our analytics team. I have about 150 rows covering multiple dataset groups with observed and expected point counts per day. Can you apply string normalisation and numeric row ordering on observed_points and return the two summary reports?"}],"resultDescription":"Two independent analytical reports covering: (1) string-normalised representations of the dataset group and row fields, and (2) numerically ordered rows based on the selected numeric field (observed_points or expected_points), derived entirely from the supplied input data without any live lookups or external data.","failureModes":["Exceeding 200 rows causes rejection — keep batch to maxItems limit","Missing required fields (row_id, date, observed_points, expected_points) will cause validation errors","Dates not matching YYYY-MM-DD pattern will be rejected","Null values in numeric fields are allowed but may affect ordering output","Invalid field selector (not 'observed_points' or 'expected_points') will fail enum validation"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline string normalisation and numeric ordering on time series completeness records (observed vs expected points) for up to 200 rows, and want two bundled analytical reports at minimal cost ($0.01 USDC). It is not suitable for live data lookups, predictions, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:35.369Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-numeric-row-ordering-6de5fd22"}