{"uid":"cap_3QGsT1d-enq4gLtNVcZbG","slug":"time-series-completeness-audit-record-quality-string-normalisation-bc7a215a","name":"Time Series Completeness Audit — Record Quality, String Normalisation & Numeric Row Ordering","description":"Time series completeness — Record quality audit + String normalisation + Numeric row ordering: Record quality audit + String normalisation + Numeric row ordering 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-16-196?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_co_NSpEJ3smACLPrGlg8G","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":"Audits up to 200 time-series completeness records (observed vs. expected points) across three reports: record quality audit, string normalisation, and numeric row ordering — all processed locally with no external lookups.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and numeric row ordering on my time series completeness data? I have about 50 rows with columns row_id, dataset_group, date, observed_points, and expected_points — I want to check the observed_points field.","exampleUseCases":[{"title":"Daily pipeline completeness health check","prompt":"I have 120 rows of daily completeness records from our data pipeline — each row has a date, dataset group, observed points, and expected points. Can you run a full record quality audit plus string normalisation and numeric ordering on the observed_points field so I can spot gaps or bad data?"},{"title":"Audit survey dataset before reporting","prompt":"Before I submit this week's survey data report, can you validate my 80 completeness records for data quality issues, normalise the dataset_group strings, and order everything by expected_points? Here are the rows with date, row_id, dataset_group, observed_points, and expected_points."},{"title":"Normalise and sort sensor coverage records","prompt":"I've got 200 rows of sensor coverage completeness logs — row_id, date, dataset_group, observed_points, and expected_points — and I need all three analytics reports: quality audit, string normalisation on the group names, and numeric ordering of the expected_points column."}],"resultDescription":"Returns three independent analytical reports for the submitted batch (up to 200 rows): (1) a record quality audit identifying malformed or problematic entries, (2) a string normalisation report for dataset_group and related string fields, and (3) a numeric row ordering report ranking rows by the selected field (observed_points or expected_points). All processing is local with no external calls.","failureModes":["Batch exceeds 200 rows — request rejected with validation error","Missing required fields (row_id, date, observed_points, expected_points) — row rejected or error returned","Invalid date format (not YYYY-MM-DD) — validation failure","Null numeric values may affect ordering report completeness","Unsupported field enum value — request rejected"],"whenToPreferThis":"Choose this endpoint when you need a combined, low-cost ($0.01 USDC) batch audit of time series completeness records covering data quality, string cleanliness, and numeric ordering in a single call — ideal for pre-reporting validation of observed vs. expected point datasets of up to 200 rows where no live prediction or external enrichment is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:32.084Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-audit-record-quality-string-normalisation-bc7a215a"}