{"uid":"cap_CP8OvUVVyZrXAt8PN_RCs","slug":"humidity-observations-data-quality-audit-string-normalisation-01bcba4c","name":"Humidity Observations Data Quality Audit, String Normalisation & Category Frequencies","description":"Humidity observations — Record quality audit + String normalisation + Category frequencies: Record quality audit + String normalisation + Category frequencies for supplied humidity observations records (humidity_percent, observation_count). 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-04-195?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","humidity_percent","observation_count"],"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},"humidity_percent":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observation_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","station_group":"group-a","humidity_percent":10,"observation_count":1},{"date":"2026-10-02","row_id":"row-2","station_group":"group-b","humidity_percent":20,"observation_count":2},{"date":"2026-10-03","row_id":"row-3","station_group":"group-a","humidity_percent":15,"observation_count":3},{"date":"2026-10-04","row_id":"row-4","station_group":"group-b","humidity_percent":30,"observation_count":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_TCov-Cfu0Mbcev0RPcfb8","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 on supplied humidity observation records: a record quality audit, string normalisation, and category frequency analysis — all offline, up to 200 rows.","exampleAgentPrompt":"Can you audit the quality of these humidity observation records, normalise the station group strings, and give me category frequencies? I have about 150 rows with fields like row_id, station_group, date, humidity_percent, and observation_count.","exampleUseCases":[{"title":"Pre-ingestion data quality check","prompt":"Before I load this humidity dataset into our data warehouse, can you run a quality audit on these 120 humidity observation rows — check for bad records, normalise the station_group strings, and show me how frequently each category appears?"},{"title":"Weather station string standardisation","prompt":"I've got humidity readings from multiple weather stations and the station_group labels are inconsistently formatted. Can you normalise those strings and also give me a quality audit and category frequency report for all 80 rows?"},{"title":"Spotting anomalies in observation counts","prompt":"Can you do a record quality audit on these 200 humidity observation records, fix up the station group strings, and break down how often each category shows up? I want to catch any bad data before I run my analysis."}],"resultDescription":"Returns three independent reports for the supplied rows: (1) a record quality audit flagging data issues, (2) normalised string values for text fields such as station_group, and (3) category frequency counts showing how often each distinct category value appears. All analysis is performed locally on the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, station_group, date, humidity_percent, observation_count) cause row-level rejection","Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure","Empty rows array returns an error","Null numeric values may be flagged in the quality audit rather than causing failure"],"whenToPreferThis":"Choose this endpoint when you need to quickly audit, clean, and summarise humidity observation records without any external data dependencies. It is ideal for pre-processing pipelines where you need all three reports (quality audit, string normalisation, category frequencies) in a single cheap call ($0.01 USDC) on batches up to 200 rows. Prefer alternatives if you need live weather lookups, predictive modelling, or processing more than 200 rows at once.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T13:15:50.760Z","isFirstParty":false,"canonicalSlug":"humidity-observations-data-quality-audit-string-normalisation-01bcba4c"}