{"uid":"cap_0HiVUzvSWpzIciaTpSqGz","slug":"forecast-verification-inputs-record-quality-audit-string-3b2274db","name":"Forecast Verification Inputs — Record Quality Audit + String Normalisation + Duplicate Key Report","description":"Forecast verification inputs — Record quality audit + String normalisation + Duplicate key report: Record quality audit + String normalisation + Duplicate key report 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-24-192?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}}},"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_XtZg4OYMkwmNQEYrlMTvr","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 duplicate key detection) on up to 200 rows of forecast-vs-observed weather verification data.","exampleAgentPrompt":"Can you run a full data quality check on my forecast verification records — I have about 50 rows with fields like row_id, station_group, date, forecast_value, and observed_value, and I need a record quality audit, string normalisation, and duplicate key report all in one go?","exampleUseCases":[{"title":"Pre-analysis data quality gate","prompt":"Before I run accuracy metrics on my weather model outputs, can you audit these 120 forecast verification rows for quality issues, normalise the station_group strings, and flag any duplicate row IDs? Here are the rows with date, station_group, forecast_value, and observed_value fields."},{"title":"Detecting duplicate station-date keys","prompt":"I suspect some of my forecast records have duplicate entries for the same station and date — can you run a duplicate key report on these 80 rows of forecast vs observed values so I can clean them up?"},{"title":"String normalisation for station group labels","prompt":"My weather station group labels are inconsistently formatted across 150 forecast verification rows — can you run a string normalisation report on them alongside a record quality audit so I know what needs fixing?"}],"resultDescription":"Three structured reports returned together: (1) a record quality audit flagging missing, null, or out-of-range values in each row; (2) a string normalisation report suggesting or applying consistent casing/formatting to station_group strings; (3) a duplicate key report identifying rows that share the same row_id or station_group/date combination. All analysis is performed locally with no external lookups.","failureModes":["Empty rows array or missing required fields (row_id, date, forecast_value, observed_value) causes validation error","More than 200 rows submitted exceeds the maxItems limit and will be rejected","Date strings not matching YYYY-MM-DD pattern will fail schema validation","Numeric values outside ±1,000,000,000,000 range will be rejected","Malformed JSON body returns an error response"],"whenToPreferThis":"Choose this endpoint when you need a quick, cheap ($0.01 USDC) pre-processing quality check on forecast verification data before running statistical accuracy metrics. It is ideal when your dataset is under 200 rows and you need three reports (quality audit, string normalisation, duplicate detection) in a single call without any external data lookups or live weather data access. Prefer this over generic data validation tools when your data specifically has forecast_value and observed_value paired with station groups and dates.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:36:06.630Z","isFirstParty":false,"canonicalSlug":"forecast-verification-inputs-record-quality-audit-string-3b2274db"}