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Texture Baking Data Quality Audit + String Normalisation + Secondary-Measure Weighted Mean
x402
Texture Baking Data Quality Audit + String Normalisation + Secondary-Measure Weighted Mean 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 texture baking records: a record quality audit, string normalisation, and a secondary-measure weighted mean — all computed locally without external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Texture baking — Record quality audit + String normalisation + Secondary-measure weighted mean: Record quality audit + String normalisation + Secondary-measure weighted mean for supplied texture baking records (bake_seconds, texture_pixels). 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 for the submitted texture baking rows: (1) a record quality audit flagging issues such as missing or malformed values, (2) normalised string representations of categorical fields like asset_group, and (3) a weighted mean computed for the selected secondary measure field (bake_seconds or texture_pixels).
Example use cases
QA pass on baking pipeline output
“I've got a batch of texture baking records — row IDs, asset groups, dates, bake_seconds, and texture_pixels. Can you audit the record quality, normalise the asset group strings, and give me a weighted mean for bake_seconds across all the rows?”
Weighted mean for texture pixel counts
“Run the three-report analysis on my texture baking dataset — I want the quality audit, string normalisation, and the weighted mean computed for texture_pixels. Here are 150 rows covering multiple asset groups.”
Pre-pipeline data validation for a render job
“Before I hand off these texture baking records to the next stage, can you do a quality audit, clean up the string fields, and compute the weighted mean for bake_seconds so I can sanity-check the batch? I have about 80 rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-10-205?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need all three of a record quality audit, string normalisation, and weighted mean in a single call for texture baking data (bake_seconds and texture_pixels), and your dataset fits within 200 rows. It is a self-contained local computation requiring no external data sources, making it fast and deterministic. Prefer this over general-purpose analytics tools when you specifically work in a 3D texture baking or rendering pipeline context.
Side effects
- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, bake_seconds, texture_pixels) cause row-level errors
- Malformed date strings (not YYYY-MM-DD) fail schema validation
- Payment of 0.01 USDC not completed results in 402 response
- Numeric values outside the allowed range trigger validation rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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