{"uid":"cap_LfUcd1Wcc-49q93kjE_hj","slug":"time-series-completeness-string-normalisation-grouped-numeric-summary-97eac58c","name":"Time Series Completeness — String Normalisation + Grouped Numeric Summary","description":"Time series completeness — String normalisation + Grouped numeric summary: String normalisation + Grouped numeric summary 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-16-023?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_a_OhjW0KO9W5l8PYGuIcD","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 produces grouped numeric summary statistics for time series completeness records (observed_points vs expected_points) across up to 200 rows.","exampleAgentPrompt":"I have a set of daily time series records across two groups — 'group-a' and 'group-b' — with dates from 2026-10-01 to 2026-10-04, and each row has observed_points and expected_points values. Can you normalise the group name strings and give me a grouped numeric summary for the observed_points field?","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I've got 150 rows of daily sensor data for October, each with a dataset_group, date, observed_points, and expected_points. Can you normalise the group names and give me a grouped numeric summary of observed_points so I can see where completeness is lowest?"},{"title":"Multi-team data ingestion review","prompt":"We have time series completeness records across 5 teams — each row has a team name as dataset_group plus observed and expected point counts. Please normalise the team name strings and produce a grouped numeric summary on expected_points for all 80 rows."},{"title":"Completeness regression check after ETL change","prompt":"After our ETL update, I want to check whether observed_points changed across our dataset groups. Here are 200 rows with dates, group labels, and both observed and expected counts — normalise the group strings and summarise the observed_points by group so I can spot any regressions."}],"resultDescription":"Returns two reports: (1) a string normalisation report for the dataset_group column, and (2) a grouped numeric summary of the selected field (observed_points or expected_points), including aggregated statistics per group — all computed locally from the supplied rows, with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected","Missing required fields (row_id, date, observed_points, expected_points) — validation error","Date string not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range — rejection","Invalid field enum value — error response","Empty rows array — schema minimum violation"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of group labels and grouped numeric summarisation of time series completeness data (observed vs expected points) in a single cheap call ($0.01 USDC for 2 reports). Ideal for data quality pipelines, completeness audits, and ETL monitoring workflows where you have up to 200 rows and need offline, deterministic analytics without any external lookup or ML inference.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:00.250Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-grouped-numeric-summary-97eac58c"}