{"uid":"cap_r2ht1p5VAb0YPf4jJiUSy","slug":"ui-screens-string-normalisation-explicit-missing-value-fill-9b0c230a","name":"UI Screens — String Normalisation + Explicit Missing-Value Fill","description":"UI screens — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied ui screens records (widget_count, render_ms). 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-game-16-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","game_area","date","widget_count","render_ms"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"game_area":{"type":"string","default":"","maxLength":80},"render_ms":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"widget_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","game_area":"group-a","render_ms":1,"widget_count":10},{"date":"2026-10-02","row_id":"row-2","game_area":"group-b","render_ms":2,"widget_count":20},{"date":"2026-10-03","row_id":"row-3","game_area":"group-a","render_ms":3,"widget_count":15},{"date":"2026-10-04","row_id":"row-4","game_area":"group-b","render_ms":4,"widget_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["widget_count","render_ms"],"type":"string","default":"widget_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_CnxLH7rrz4snxcwfh7woc","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":"Normalises string fields and fills missing numeric values (widget_count or render_ms) in UI screen records using zero, mean, or median imputation, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of UI screen records with widget_count and render_ms fields — some render_ms values are missing. Can you run string normalisation and fill in the missing render_ms values using median imputation, and give me two analytical reports?","exampleUseCases":[{"title":"Impute missing widget counts by median","prompt":"I've got a batch of UI screen rows where widget_count has some nulls — can you fill those missing values using median imputation and also normalise all the string fields? Here are my rows with row_id, game_area, date, and the numeric fields."},{"title":"Zero-fill missing render times for game area","prompt":"My game UI dataset has render_ms values missing for several rows in group-a and group-b. Please fill those gaps with zero and normalise the strings, then give me the two analytical reports."},{"title":"Mean imputation for widget counts before analysis","prompt":"Before I run my analysis, I need to clean my UI screen records — please normalise the string fields and fill in any missing widget_count values using the mean. I have about 150 rows with row_id, game_area, date, widget_count, and render_ms."}],"resultDescription":"Returns two independent analytical reports based on the supplied UI screen records: one covering string normalisation results and one covering explicit missing-value fill for the chosen numeric field (widget_count or render_ms) using the selected method (zero, mean, or median). No external lookups or predictions are performed; all computation uses local analytical engines.","failureModes":["Rows array empty or exceeds 200 items — validation error returned","Invalid date format (not YYYY-MM-DD) — schema validation failure","Unknown imputation method value outside zero/mean/median enum — rejected","Numeric field values outside allowed range — validation error","Missing required fields (row_id, game_area, date) in any row — error response","Payment of 0.01 USDC not provided or rejected — 402 payment required"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local data cleaning on UI screen datasets of up to 200 rows — specifically combining string normalisation with explicit missing-value fill for widget_count or render_ms in a single call. Prefer it over general-purpose ETL tools when you want two ready-made analytical reports at low cost ($0.01 USDC) without sending data to external prediction or ML services.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:33:18.370Z","isFirstParty":false,"canonicalSlug":"ui-screens-string-normalisation-explicit-missing-value-fill-9b0c230a"}