{"uid":"cap_0a3ao3pM7ItnBqYU_Zte7","slug":"forecast-verification-inputs-record-quality-audit-string-f927fa44","name":"Forecast Verification Inputs — Record Quality Audit + String Normalisation + Adjacent Row Changes","description":"Forecast verification inputs — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied forecast verification inputs records (forecast_value, observed_value). 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-weather-24-200?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","forecast_value","observed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"station_group":{"type":"string","default":"","maxLength":80},"forecast_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","station_group":"group-a","forecast_value":10,"observed_value":1},{"date":"2026-10-02","row_id":"row-2","station_group":"group-b","forecast_value":20,"observed_value":2},{"date":"2026-10-03","row_id":"row-3","station_group":"group-a","forecast_value":15,"observed_value":3},{"date":"2026-10-04","row_id":"row-4","station_group":"group-b","forecast_value":30,"observed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["forecast_value","observed_value"],"type":"string","default":"forecast_value"}}},"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_40-ZysHspgad1ImIHGdte","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 local analytical reports (record quality audit, string normalisation, and adjacent row changes) on up to 200 forecast verification input rows containing forecast and observed values.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation check, and adjacent row changes analysis on my forecast verification data? I have rows with station_group, date, forecast_value, and observed_value fields — here are the 10 rows I want checked, and I want to analyse the forecast_value column.","exampleUseCases":[{"title":"Weather forecast accuracy data audit","prompt":"I have a batch of 50 forecast verification records with station groups and dates from October 2026 — can you run a quality audit, string normalisation, and adjacent row changes report on the forecast_value field to see if there are any data issues before I use them for analysis?"},{"title":"Detecting abrupt observed value shifts","prompt":"My team has 120 rows of observed temperature readings paired with forecast values across multiple station groups — can you check for adjacent row changes in the observed_value field and flag anything that looks like a sudden jump or drop?"},{"title":"Station group name normalisation check","prompt":"We've got forecast verification inputs from about 80 rows collected across different teams and the station_group naming is probably inconsistent — run a string normalisation report on these records so I can see what needs to be cleaned up."}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values; (2) a string normalisation report identifying inconsistencies or formatting issues in text fields like station_group; and (3) an adjacent row changes report highlighting sequential differences between consecutive rows for the specified numeric field (forecast_value or observed_value). All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, forecast_value, observed_value) on any row will cause validation failure","Date fields not matching YYYY-MM-DD pattern will fail schema validation","Payment of 0.01 USDC not completed via x402 protocol — endpoint returns 402","Supplying an invalid enum value for the 'field' parameter (must be 'forecast_value' or 'observed_value')"],"whenToPreferThis":"Choose this endpoint when you have tabular forecast verification data (forecast vs observed numeric pairs) and need a combined data quality audit, string cleaning report, and sequential change detection in a single low-cost call. It is ideal for pre-analysis data validation pipelines where you want to catch record quality issues, naming inconsistencies, and sudden value shifts without sending data to an external live service or prediction engine.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T12:53:40.020Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-record-quality-audit-string-f927fa44"}