{"uid":"cap_62CzYyic1mJnO6jXhIUn_","slug":"time-series-completeness-analyzer-string-normalisation-adjacent-row-da0eff67","name":"Time Series Completeness Analyzer — String Normalisation & Adjacent Row Changes","description":"Time series completeness — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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-16-029?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_kmcJt2KcB_cZdYbUjEI26","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 time series completeness records by applying string normalisation and detecting adjacent row changes across observed_points and expected_points fields, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 200 rows of time series completeness data with dates, dataset groups, observed_points, and expected_points — can you run string normalisation and adjacent row change detection on the observed_points field and give me both reports?","exampleUseCases":[{"title":"Audit daily pipeline completeness gaps","prompt":"I have daily completeness records for my ETL pipeline — each row has a date, dataset_group, observed_points, and expected_points. Can you normalize the strings and flag where consecutive rows show changes in observed_points so I can spot missing data runs?"},{"title":"Detect sensor data drop-offs by group","prompt":"Here are 150 rows of sensor completeness metrics grouped by sensor_id across dates, with observed and expected point counts. Run string normalisation on the dataset_group field and detect adjacent row changes in expected_points so I can identify where sensors dropped off."},{"title":"QA check on reporting completeness records","prompt":"Before I publish my weekly completeness report, can you take these 80 rows — each with a row_id, date, dataset_group, observed_points, and expected_points — and give me the string normalisation report plus the adjacent row change report on observed_points?"}],"resultDescription":"Returns two analytical reports for the submitted rows: (1) a string normalisation report identifying inconsistent or denormalized string values in the dataset_group and related fields, and (2) an adjacent row changes report flagging where consecutive rows show value shifts in the selected field (observed_points or expected_points). Both reports are computed locally without external lookups or live data enrichment.","failureModes":["Exceeds 200 row limit — request rejected","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Invalid date format (must be YYYY-MM-DD) — schema validation failure","Null values in observed_points or expected_points may affect adjacent change calculations","Invalid field enum value (must be 'observed_points' or 'expected_points') — rejected"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness rows and need both string normalisation analysis and adjacent row change detection in a single paid call. Ideal for data quality audits on pipeline completeness logs where you want to identify naming inconsistencies in dataset groups and spot consecutive-row anomalies in point counts without making external API calls or requiring live data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:43.773Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-analyzer-string-normalisation-adjacent-row-da0eff67"}