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Save Files Analyzer — String Normalisation + Date-by-Category Pivot
x402
Save Files Analyzer — String Normalisation + 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 2026-10-30).
Runs two analytical reports on game save-file records: string normalisation of categorical fields and a date-by-category pivot for a chosen numeric metric (save_kb or write_ms), on up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — String normalisation + Date by category pivot: String normalisation + 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
Two structured reports: (1) a string-normalised view of the game_area categorical field across all supplied rows, and (2) a date-by-category pivot table showing aggregated values of the chosen numeric field (save_kb or write_ms) broken down by date and game_area. All computation is performed locally on the supplied data — no external lookups or predictions.
Example use cases
Game save size trends by area
“I've got 150 save file records with columns row_id, game_area, date, and save_kb. Can you normalize the game_area strings and pivot the save_kb values by date and game_area so I can see trends?”
Write latency breakdown by category
“Run the two analytical reports on my save file data — string normalisation on game_area and a date-by-category pivot using write_ms as the metric. I have around 80 rows.”
Clean and summarize mixed save records
“My game save export has inconsistent area labels and I want to see how save_kb stacks up across dates and categories. Can you normalize the strings and generate the date pivot report for my 200 rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have game save-file records (up to 200 rows) and need both string normalisation of categorical labels and a date-by-category pivot on a numeric metric in a single low-cost call. It is ideal for lightweight, offline analytical summaries of save performance data without needing a full data warehouse or live lookup service.
Side effects
- Rows exceed 200-item limit — request rejected
- Missing required fields (row_id, game_area, date, save_kb, write_ms) — validation error
- Invalid date format (must match YYYY-MM-DD) — schema rejection
- Numeric values outside allowed range — validation failure
- Empty rows array — minimum 1 item required
- Invalid field selector (must be save_kb or write_ms) — enum validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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