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Material Libraries Data Quality Audit: Record Quality Audit + String Normalisation + Explicit Missing-Value Fill
x402
Material Libraries Data Quality Audit: Record Quality Audit + String Normalisation + 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 2026-10-22).
Runs three analytical reports on material library records — a record quality audit, string normalisation, and explicit missing-value fill — for up to 200 rows of material_count and texture_mb data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Material libraries — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied material libraries records (material_count, texture_mb). 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
Returns three independent analytical reports: (1) a record quality audit flagging data issues in the supplied rows, (2) a string normalisation report for text fields such as asset_group, and (3) an explicit missing-value fill report for the specified numeric field (material_count or texture_mb) using the chosen fill method (zero, mean, or median). All reports are computed locally with no external lookups.
Example use cases
Audit game asset material library
“I have a list of up to 150 game asset records with material_count and texture_mb values — can you run a full quality audit, normalise the asset_group strings, and fill any missing material_count values using the median? Here are the rows.”
Clean missing texture sizes in 3D library
“My 3D material library export has some null texture_mb entries. Can you run the record quality audit, string normalisation, and fill those missing texture_mb values using zero for up to 200 rows I'll give you?”
Validate material records before pipeline import
“Before I import these material library rows into our pipeline, can you audit them for quality issues, normalise the strings, and fill any missing material_count fields using the mean? I've got about 80 rows ready.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-03-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a combined data quality audit, string normalisation, and missing-value fill on material library records (material_count, texture_mb) in a single low-cost call. It is ideal for creative/3D production pipelines where asset data needs cleaning before downstream processing, and where no live external lookup or predictive model is needed — just fast, local analytical reports on up to 200 rows.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Invalid date format (must be YYYY-MM-DD) — validation error
- Unrecognised fill method enum value — schema validation error
- Numeric field values outside acceptable range — schema rejection
- Empty rows array — minimum 1 row required
- Missing required fields (row_id, asset_group, date) — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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