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Save Files Data Quality Audit — String Normalisation & Numeric Interval Filter
x402
Save Files Data Quality Audit — String Normalisation & Numeric Interval Filter is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-11-03).
Audits record quality, normalises strings, and filters by numeric interval for save-file records containing save_kb and write_ms fields, returning 3 independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Save files — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter 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 covering: (1) a record quality audit identifying malformed, missing, or inconsistent entries; (2) a string normalisation report detailing transformations applied to text fields like game_area; and (3) a numeric interval filter report showing which rows fall within or outside the specified minimum–maximum range for the chosen field (save_kb or write_ms). All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Game save size range validation
“I have 120 game save records and I want to audit their quality, normalise the game_area strings, and filter out any rows where save_kb falls outside 10 to 800 — can you run all three reports on this dataset?”
Write time outlier detection
“Can you check these 80 save file rows for record quality issues, clean up the string fields, and flag any entries where write_ms is below 0 or above 5000 so I can spot outliers?”
Pre-pipeline data cleansing for save records
“Before I load these 200 save file records into our pipeline, run a quality audit, normalise the strings, and filter the write_ms field to only keep rows between 1 and 2000 — give me all three reports.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-game-13-197?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of record quality auditing, string normalisation, and numeric interval filtering applied together to game save file records in a single cost-efficient call. It is ideal for batch pre-processing up to 200 rows of save file data before ingestion into a pipeline, and particularly suited when working with game telemetry data structured around save_kb and write_ms metrics. Prefer it over general-purpose data tools when you want bundled, local analytical reports without any external data lookups or live enrichment.
Side effects
- Empty or missing rows array returns an error
- More than 200 rows submitted — request rejected with maxItems violation
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- minimum greater than maximum yields no rows passing the interval filter
- Unrecognised field value (not save_kb or write_ms) causes enum validation error
- Null numeric values in save_kb or write_ms may be flagged in the quality audit report
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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