{"uid":"cap_MlfnnP0IXbgKlaK7-XMnQ","slug":"time-series-completeness-record-quality-audit-string-normalisation-86ca2387","name":"Time Series Completeness — Record Quality Audit + String Normalisation + Adjacent Row Changes","description":"Time series completeness — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-200?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_0Isl9a_AeYZXX7P9FdeVA","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 three analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 time series completeness records containing observed and expected data points.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and adjacent row changes analysis on my time series completeness data? I have rows with dataset_group, date, observed_points, and expected_points — here are up to 200 rows and I want all three reports at once.","exampleUseCases":[{"title":"Auditing sensor data completeness records","prompt":"I have daily sensor readings with observed and expected data points for groups 'sensor-a' and 'sensor-b' across October 2026 — can you run a quality audit, normalise the group name strings, and flag any adjacent row changes in observed_points for up to 150 rows?"},{"title":"Validating ETL pipeline completeness logs","prompt":"Our ETL pipeline logs expected vs observed data points per dataset group each day — please audit these 80 rows for record quality issues, normalise the group label strings, and show me where observed_points changed between consecutive rows."},{"title":"Monitoring data feed coverage gaps","prompt":"I want to check our data feed coverage records for the last 200 days — run the three standard reports (record quality, string normalisation, adjacent row changes) on the observed_points field across all dataset groups so I can spot gaps and inconsistencies."}],"resultDescription":"Three independent analytical reports covering: (1) a record quality audit flagging issues like nulls, out-of-range values, or malformed fields; (2) a string normalisation report on dataset_group and other string fields; and (3) an adjacent row changes report showing where values in the selected field (observed_points or expected_points) differ between consecutive rows. All derived locally from the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, observed_points, expected_points) causes rejection","Malformed date strings not matching YYYY-MM-DD pattern cause field-level errors","Numeric values outside the min/max bounds are rejected","Empty rows array is not accepted (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you need three bundled data quality checks — record quality audit, string normalisation, and adjacent row changes — on time series completeness records (observed vs expected points) in a single low-cost call. Ideal when you have up to 200 date-keyed rows and want offline, deterministic analytical reports without any external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:49.845Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-record-quality-audit-string-normalisation-86ca2387"}