{"uid":"cap_Jd6x4lR6VJhImuv4qiTKk","slug":"elevation-samples-record-quality-audit-string-normalisation-adjacent-0664df94","name":"Elevation Samples — Record Quality Audit + String Normalisation + Adjacent Row Changes","description":"Elevation samples — Record quality audit + String normalisation + Adjacent row changes: Record quality audit + String normalisation + Adjacent row changes for supplied elevation samples records (elevation_m, sample_weight). 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-13-200?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","elevation_m","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"elevation_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","elevation_m":10,"sample_weight":1,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","elevation_m":20,"sample_weight":2,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","elevation_m":15,"sample_weight":3,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","elevation_m":30,"sample_weight":4,"station_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["elevation_m","sample_weight"],"type":"string","default":"elevation_m"}}},"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_FYuHUnwRUqj-Sct-_EZvG","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 elevation sample records containing elevation_m and sample_weight fields.","exampleAgentPrompt":"Can you run a quality audit, string normalisation, and adjacent row change analysis on my elevation sample data? I have about 50 rows with elevation_m and sample_weight readings across multiple station groups, and I want to focus the adjacent row changes report on the elevation_m field.","exampleUseCases":[{"title":"Pre-analysis data quality check","prompt":"I have 120 elevation sample records from our field survey with station_group labels that may have inconsistent formatting — can you audit the record quality, normalise those strings, and show me how elevation_m values change between consecutive rows?"},{"title":"Detecting sequential anomalies in elevation readings","prompt":"Run an adjacent row changes report on my elevation_m field for these 80 elevation samples grouped by station — I want to spot any sudden jumps between consecutive measurements and flag quality issues at the same time."},{"title":"Cleaning station group labels before aggregation","prompt":"Before I aggregate my elevation data, can you check my 60 sample records for quality problems and normalise the station_group strings so inconsistent capitalisations or whitespace don't mess up my grouping?"}],"resultDescription":"Three independent analytical reports returned together: (1) a record quality audit flagging nulls, out-of-range values, and structural issues; (2) a string normalisation report showing standardised forms of station_group and date strings; (3) an adjacent row changes report highlighting differences between consecutive rows for the selected field (elevation_m or sample_weight). All analysis is performed locally with no external lookups.","failureModes":["Empty rows array or missing required fields (row_id, station_group, date, elevation_m, sample_weight) returns validation error","More than 200 rows submitted causes rejection due to maxItems constraint","Invalid date format (not YYYY-MM-DD) fails schema pattern validation","Invalid field enum value (not elevation_m or sample_weight) causes request rejection","Payment failure via x402 protocol prevents processing"],"whenToPreferThis":"Choose this endpoint when you need three combined data-quality operations on elevation sample records in a single low-cost call — specifically record auditing, string normalisation, and adjacent row change detection. Prefer this over building custom scripts when working with up to 200 rows of elevation_m and sample_weight data and needing fast, offline analytical reports without any external data lookup or live prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:38:21.593Z","isFirstParty":false,"canonicalSlug":"elevation-samples-record-quality-audit-string-normalisation-adjacent-0664df94"}