{"uid":"cap_GdGhZKXQ7DXcfmnvEOYtu","slug":"ui-screens-adjacent-row-changes-explicit-missing-value-fill-f4e201d8","name":"UI Screens Adjacent Row Changes + Explicit Missing-Value Fill","description":"UI screens — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + 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-16-162?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_azgTBXqO8Q8U7YgkoZfX3","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 UI screen data (widget_count, render_ms) by computing adjacent row changes and explicitly filling missing values using zero, mean, or median imputation — returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of UI screen data with row_id, game_area, date, widget_count, and render_ms — some values are null. Can you run adjacent row change analysis plus median missing-value fill on the widget_count field and give me both reports?","exampleUseCases":[{"title":"Game area render time delta analysis","prompt":"I've got daily render_ms readings across different game areas in my app — some rows have nulls. Can you compute the adjacent row changes and fill the missing render_ms values using mean imputation? Here are up to 150 rows of data."},{"title":"Widget count gap filling for dashboard","prompt":"My UI screen dataset has missing widget_count entries scattered across rows. Can you run an adjacent row change report and fill those gaps using median imputation on the widget_count field for my 80 records?"},{"title":"QA review of sequential UI metric shifts","prompt":"I need to audit my UI screen logs for sudden jumps between consecutive rows and also patch any null widget_count values using zero-fill. Can you produce both reports for these 200 rows of screen data?"}],"resultDescription":"Two analytical reports: (1) an adjacent row change report showing the difference between each consecutive row for the selected field (widget_count or render_ms), and (2) an explicit missing-value fill report showing imputed values using the chosen method (zero, mean, or median). Both reports are computed locally against the supplied rows with no external lookups.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, game_area) cause row-level errors","Invalid date format (must be YYYY-MM-DD) causes parse failure","Invalid method enum value (not zero/mean/median) causes rejection","All values null for selected field makes mean/median imputation undefined","Payment of 0.01 USDC not attached or rejected causes 402 error"],"whenToPreferThis":"Choose this endpoint when you have UI screen records (with widget_count or render_ms metrics) and need both sequential delta analysis and missing value imputation in a single low-cost call. Ideal for small-to-medium datasets up to 200 rows where you want offline, deterministic computation without external API dependencies. Prefer this over general-purpose analytics tools when you need the two specific report types (adjacent changes + explicit fill) bundled together at a fixed micro-payment cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:37.042Z","isFirstParty":false,"canonicalSlug":"ui-screens-adjacent-row-changes-explicit-missing-value-fill-f4e201d8"}