{"uid":"cap_vlQosUEW7fA0pg5hnzcr0","slug":"coordinate-quality-checks-adjacent-row-changes-iqr-outlier-flags-de9b5158","name":"Coordinate Quality Checks — Adjacent Row Changes + IQR Outlier Flags","description":"Coordinate quality checks — Adjacent row changes + IQR outlier flags: Adjacent row changes + IQR outlier flags 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-23-157?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"}}},"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_bMkB09qsz5OEaVNU5k0pi","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 records, producing two independent reports: adjacent row change detection and IQR-based outlier flagging for checked_points or flagged_points fields.","exampleAgentPrompt":"Run adjacent row change detection and IQR outlier flagging on my coordinate quality data — I have 4 rows with station groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, checked_points values of 10, 20, 15, 30 and flagged_points of 1, 2, 3, 4 — analyze the checked_points field.","exampleUseCases":[{"title":"Spike detection in daily QC runs","prompt":"I have daily coordinate quality check records for two station groups over the past month — can you run adjacent row change analysis and IQR outlier detection on the checked_points field to find any days with suspicious spikes or sudden jumps?"},{"title":"Audit flagged points for anomalies","prompt":"Here are 150 rows of coordinate quality check data with station groups and dates — please analyze the flagged_points field using IQR outlier flagging and adjacent row change reports so I can see which records are statistically unusual."},{"title":"Pre-release data sanity check","prompt":"Before I publish this coordinate quality dataset, run an IQR outlier check and adjacent row change analysis on the checked_points column across all 80 rows — I want to catch any bad values before they go out."}],"resultDescription":"Two independent analytical reports: (1) an adjacent row change report showing differences between consecutive rows for the selected field, and (2) an IQR outlier flag report identifying rows whose values fall outside the interquartile range bounds — both computed locally on the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — minimum row count not met","More than 200 rows supplied — exceeds maximum batch size","Missing required fields (row_id, station_group, date, checked_points, or flagged_points) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Null values in the selected field may reduce statistical reliability of IQR computation","Invalid field enum value (must be 'checked_points' or 'flagged_points') — rejected input"],"whenToPreferThis":"Choose this endpoint when you have tabular coordinate quality check records (checked_points, flagged_points) and need both adjacent row change detection and IQR-based outlier flagging in a single low-cost call. It is well-suited for batch audits of up to 200 rows where no live data lookup or external enrichment is needed — just local statistical analysis on your supplied data. Prefer it over general-purpose analytics when your data specifically tracks coordinate quality metrics by station group and date.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:37:05.668Z","isFirstParty":false,"canonicalSlug":"coordinate-quality-checks-adjacent-row-changes-iqr-outlier-flags-de9b5158"}