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Save Files Analytics — Record Quality Audit + String Normalisation + Trailing Mean Series
x402
Save Files Analytics — Record Quality Audit + String Normalisation + Trailing Mean Series 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 three local analytical reports (record quality audit, string normalisation, and trailing mean series) on game save file metrics (save_kb, write_ms) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Record quality audit + String normalisation + Trailing mean series: Record quality audit + String normalisation + Trailing mean series for supplied save files records (save_kb, write_ms). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent analytical reports: (1) a record quality audit flagging missing, null, or out-of-range values; (2) a string normalisation report standardising text fields like game_area; (3) a trailing mean series showing rolling averages of the chosen numeric field (save_kb or write_ms) over the specified window. All computed locally without external lookups.
Example use cases
Game save data quality check
“I have 120 rows of game save file data with save_kb and write_ms values logged by game_area and date — can you run a record quality audit plus string normalisation on them and flag any issues?”
Rolling average of write latency
“Can you compute a trailing mean series with a window of 5 for the write_ms field across my game save records? I've got about 80 rows covering different game areas from the past month.”
Clean and summarise save file metrics
“Run all three reports — record quality audit, string normalisation, and trailing mean — on my save file dataset of 60 rows, focusing on the save_kb field with a window of 4, so I can see data quality and trend in one go.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-199?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three analytical passes on game save file records in a single low-cost call: data quality auditing, string normalisation, and rolling mean computation. It is ideal when data is already collected and you need offline/local analysis without any live lookup, prediction, or external action. Best for datasets up to 200 rows of game telemetry with save size and write latency metrics.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds maximum limit
- Invalid date format (not YYYY-MM-DD) — validation error
- Missing required fields (row_id, game_area, date, save_kb, write_ms) — schema error
- Window size outside 2–200 range — parameter error
- Field value not in enum ['save_kb','write_ms'] — rejected
- Payment not attached or insufficient — 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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