{"uid":"cap_-NgEIRVsKdOaSrLB00j4t","slug":"time-series-completeness-string-normalisation-numeric-distribution-15421b0f","name":"Time Series Completeness — String Normalisation + Numeric Distribution","description":"Time series completeness — String normalisation + Numeric distribution: String normalisation + Numeric distribution 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-16-022?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_td38JUCOxKBp2_by4HDFv","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":"Runs string normalisation and numeric distribution analysis on time series completeness records (observed_points, expected_points) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Can you run string normalisation and numeric distribution analysis on my time series completeness data? I have rows with dataset_group, date, observed_points, and expected_points — here are up to 200 records, and I want to analyze the observed_points field.","exampleUseCases":[{"title":"Data pipeline completeness audit","prompt":"I have daily completeness records for two dataset groups over the past quarter — each row has a date, observed_points, and expected_points. Can you run string normalisation and numeric distribution analysis on these so I can see where my pipeline is dropping data?"},{"title":"Distribution check on expected point counts","prompt":"Here are 150 rows of time series completeness records with row_id, dataset_group, date, observed_points, and expected_points. Please analyze the expected_points field — I want string normalisation and a numeric distribution breakdown across those rows."},{"title":"Multi-group completeness comparison","prompt":"I'm tracking sensor data completeness across groups A and B for the last 6 months. Can you take these completeness records and give me both a string normalisation report and a numeric distribution report on observed_points?"}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report covering the dataset_group and date fields, and (2) a numeric distribution report for the selected numeric field (observed_points or expected_points) across all supplied rows. No live lookups or predictions are made; all analysis is performed on the supplied data only.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range — rejection","Empty rows array — invalid input error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local analytical computation on time series completeness data (observed vs expected points) without any external lookups or live data access. It is ideal for batch auditing up to 200 rows of completeness records and getting both string normalisation and numeric distribution insights in a single low-cost call ($0.01 USDC for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:48.233Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-numeric-distribution-15421b0f"}