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Save Files Analyzer — Adjacent Row Changes + Date by Category Pivot
x402
Save Files Analyzer — Adjacent Row Changes + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-06).
Analyzes save file metrics (save size and write time) by computing adjacent row changes and a date-by-category pivot table for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot for supplied save files records (save_kb, write_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
Returns two analytical reports: (1) an adjacent row changes report showing the difference in the selected metric (save_kb or write_ms) between each consecutive row, and (2) a date-by-category pivot table summarizing the selected metric grouped by date and game_area. Both reports cover the supplied rows (up to 200).
Example use cases
Game save size trend analysis
“I have a list of 80 game save records with dates, game areas, save sizes in KB, and write times in milliseconds — can you run the adjacent row changes and date-by-category pivot reports on save_kb to see how save file sizes shift between rows and vary by game area over time?”
Write performance comparison across areas
“Here are 120 rows of save file data with row IDs, game areas, dates, save_kb, and write_ms — please analyze write_ms using the adjacent row changes and date-category pivot reports so I can see how write times differ across game zones day by day.”
Spotting anomalies in save file metrics
“I've got 40 save file records from our QA session across different game areas — run the adjacent row changes and date-by-category pivot on save_kb so I can spot any unusual spikes or drops between consecutive saves and across categories.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-160?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured game save file records and need both a sequential row-change analysis and a date-by-category pivot in a single call, without requiring any external data lookup or live prediction. It is ideal for offline batch analysis of up to 200 rows of save metrics at minimal cost ($0.01 USDC for both reports).
Side effects
- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, game_area, date, save_kb, write_ms) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes parsing failure
- Field selector not one of 'save_kb' or 'write_ms' causes enum validation error
- Empty rows array causes a minimum items error
- Numeric values outside allowed range cause validation errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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