{"uid":"cap_Hrb_ByKcOXVU48GZc-Vpg","slug":"uv-layouts-data-quality-pipeline-record-audit-string-normalisation-753c409c","name":"UV Layouts Data Quality Pipeline: Record Audit + String Normalisation + Missing-Value Fill","description":"UV layouts — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied uv layouts records (island_count, occupied_percent). 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-creative-06-208?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","island_count","occupied_percent"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"asset_group":{"type":"string","default":"","maxLength":80},"island_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"occupied_percent":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","island_count":10,"occupied_percent":1},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","island_count":20,"occupied_percent":2},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","island_count":15,"occupied_percent":3},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","island_count":30,"occupied_percent":4}],"maxItems":200,"minItems":1},"field":{"enum":["island_count","occupied_percent"],"type":"string","default":"island_count"},"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_sqG0oDP1kJ4qFjLFg_2ZH","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 UV layout records — a record quality audit, string normalisation, and explicit missing-value fill for island_count and occupied_percent fields — on up to 200 rows using local engines.","exampleAgentPrompt":"Can you run a full data quality pipeline on my UV layout records — audit record quality, normalise strings, and fill any missing island_count values using the median method? Here are up to 200 rows with row_id, asset_group, date, island_count, and occupied_percent fields.","exampleUseCases":[{"title":"Pre-pipeline UV layout data audit","prompt":"I have a batch of UV layout records with island_count and occupied_percent fields — can you run a record quality audit on them and flag any issues before I pass them downstream? I've got about 150 rows with row_id, asset_group, and date included."},{"title":"Missing island_count imputation with mean","prompt":"Some of my UV layout rows are missing island_count values. Can you fill those gaps using the mean of the available values across my dataset? I'll give you up to 200 rows with row_id, asset_group, date, island_count, and occupied_percent."},{"title":"String normalisation for asset group labels","prompt":"My UV layout asset_group strings are inconsistent across rows — can you normalise them along with doing a quality audit and filling any missing occupied_percent values using zero? I have around 80 rows I'd like cleaned up."}],"resultDescription":"Returns three independent analytical reports for the submitted UV layout rows: (1) a record quality audit flagging completeness and consistency issues, (2) a string normalisation report for text fields, and (3) an explicit missing-value fill report for the specified numeric field (island_count or occupied_percent) using the chosen fill method (zero, mean, or median). All processing is local with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, date, asset_group) causes row-level rejection","Invalid date format (not YYYY-MM-DD) triggers a pattern validation error","Numeric values outside the allowed range cause rejection","Invalid enum values for field or method parameters return a schema error","Empty rows array is not accepted (minItems: 1)"],"whenToPreferThis":"Choose this endpoint when you have UV layout tabular data with island_count or occupied_percent fields and need a combined data quality audit, string normalisation, and missing-value imputation in a single low-cost call ($0.01 USDC). It is ideal for pre-processing pipelines where no external lookup or prediction is needed, just local analytical cleaning of up to 200 rows. Prefer alternatives if you need outlier detection, correlation analysis, or live data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-27T00:44:14.281Z","isFirstParty":false,"canonicalSlug":"uv-layouts-data-quality-pipeline-record-audit-string-normalisation-753c409c"}