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UI Screens Adjacent Row Changes + Explicit Missing-Value Fill
x402
UI Screens Adjacent Row Changes + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
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.
Price / call$0.01
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
Game area render time delta analysis
“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.”
Widget count gap filling for dashboard
“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?”
QA review of sequential UI metric shifts
“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?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-16-162?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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