{"uid":"cap_W_YKyoBgVqgaNyrX361fY","slug":"coordinate-quality-checks-adjacent-row-changes-explicit-missing-value-79c81489","name":"Coordinate Quality Checks — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Coordinate quality checks — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied coordinate quality checks records (checked_points, flagged_points). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-weather-23-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","checked_points","flagged_points"],"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},"checked_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"flagged_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","station_group":"group-a","checked_points":10,"flagged_points":1},{"date":"2026-10-02","row_id":"row-2","station_group":"group-b","checked_points":20,"flagged_points":2},{"date":"2026-10-03","row_id":"row-3","station_group":"group-a","checked_points":15,"flagged_points":3},{"date":"2026-10-04","row_id":"row-4","station_group":"group-b","checked_points":30,"flagged_points":4}],"maxItems":200,"minItems":1},"field":{"enum":["checked_points","flagged_points"],"type":"string","default":"checked_points"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_q2ZxTkVNXoclDkspWvMct","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":"Analyzes up to 200 rows of coordinate quality check data (checked_points, flagged_points) by computing adjacent row changes and filling missing values using zero, mean, or median imputation.","exampleAgentPrompt":"Take my coordinate quality check records for station groups A and B from October 2026 — with checked_points and flagged_points per row — and run both an adjacent row changes analysis and a median missing-value fill on the checked_points field, up to 200 rows.","exampleUseCases":[{"title":"Weather station QC gap filling","prompt":"I have daily quality check records for my weather stations with some null checked_points values — can you fill those missing values using the median and also show me how the values change from one row to the next?"},{"title":"Flagged points trend analysis","prompt":"Run an adjacent row changes report on my flagged_points data across station groups for the past month, and also fill any missing flagged_points values with zero so I can see the full picture."},{"title":"Multi-station coordinate QC audit","prompt":"I've got 150 rows of coordinate QC data across several station groups dated from 2026-10-01 onward — please compute row-to-row deltas and fill missing checked_points using the mean, then give me both reports."}],"resultDescription":"Two analytical reports: (1) adjacent row changes showing row-to-row deltas for the selected field across the supplied records, and (2) a version of the dataset with missing values in the selected field explicitly filled using the chosen method (zero, mean, or median). Results are based entirely on the supplied data with no external lookups.","failureModes":["Fewer than 1 or more than 200 rows submitted — request rejected","Invalid date format (not YYYY-MM-DD) causes row validation failure","Non-numeric or out-of-range values for checked_points or flagged_points","Invalid enum for method (must be zero, mean, or median) or field (must be checked_points or flagged_points)","Payment not included or insufficient USDC — x402 payment required before processing"],"whenToPreferThis":"Choose this endpoint when you need lightweight, offline analytical computation on coordinate quality check data — specifically adjacent row change detection and missing-value imputation — without requiring live data lookup, prediction, or external API calls. Best suited for weather station QC pipelines where you want two quick reports from a single low-cost call on up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:35:14.087Z","isFirstParty":false,"canonicalSlug":"coordinate-quality-checks-adjacent-row-changes-explicit-missing-value-79c81489"}