{"uid":"cap_1oO54NZLn1Iv4mft0iUzb","slug":"time-series-completeness-string-normalisation-numeric-interval-filter-bb0346ad","name":"Time Series Completeness — String Normalisation & Numeric Interval Filter","description":"Time series completeness — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter 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-16-026?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_Oh4Rz97AtdJDKpYDWkivT","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":"Normalises dataset group strings and filters time series completeness records (observed_points, expected_points) by a numeric interval, returning up to 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you analyze my time series completeness records — I have rows with dataset groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, and observed/expected point counts — and filter to only rows where observed_points is between 5 and 25, with normalized group labels?","exampleUseCases":[{"title":"Filter incomplete sensor readings by range","prompt":"I have 50 daily sensor completeness records with observed_points and expected_points from January 2025. Can you normalize the dataset group names and give me a report showing only the rows where observed_points falls between 10 and 100?"},{"title":"Audit data pipeline completeness gaps","prompt":"Here are my data pipeline completeness logs for Q3 — each row has a date, a group name, and observed vs expected point counts. Run string normalization on the group labels and filter down to rows where expected_points is between 0 and 500."},{"title":"Quality check for time series batch upload","prompt":"Before I submit this batch of 150 time series completeness rows, can you normalize the dataset_group strings and flag any rows where observed_points is outside the range of 1 to 200? I want two separate reports."}],"resultDescription":"Returns up to 2 analytical reports: one applying string normalisation to dataset_group labels and one filtering the submitted completeness rows (observed_points or expected_points) to only those within the specified minimum–maximum numeric interval, across up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, observed_points, expected_points) cause validation errors","Invalid date format (not YYYY-MM-DD) causes schema rejection","Minimum greater than maximum produces empty filter results","Null numeric values may be excluded from interval filter silently","Payment of 0.01 USDC not processed — endpoint returns 402"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline analytical processing of time series completeness data — specifically string normalisation of group labels and numeric interval filtering of observed or expected point counts — without any external lookup, prediction, or live data enrichment. Ideal for data quality audits, pipeline completeness checks, and pre-submission validation of small-to-medium batches (up to 200 rows).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:39.548Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-numeric-interval-filter-bb0346ad"}