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Render Jobs — Record Quality Audit + String Normalisation + IQR Outlier Flags
x402
Render Jobs — Record Quality Audit + String Normalisation + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-17).
Performs three bundled analytical reports on render job records: record quality audit, string normalisation, and IQR-based outlier flagging for render_seconds and sample_count fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Render jobs — Record quality audit + String normalisation + IQR outlier flags: Record quality audit + String normalisation + IQR outlier flags for supplied render jobs records (render_seconds, sample_count). 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 identifying missing, null, or malformed values per row; (2) string normalisation results for text fields like asset_group; and (3) IQR-based outlier flags indicating which rows fall outside the interquartile range for the selected numeric field (render_seconds or sample_count). All analysis is performed locally on the supplied data with no external lookups.
Example use cases
Outlier detection in render pipeline
“I have a batch of 120 render job records with render_seconds and sample_count values — can you run IQR outlier flagging on the render_seconds field and tell me which rows are statistical anomalies?”
Data quality audit before reporting
“Before I send this render job dataset to the client, can you do a record quality audit on these 80 rows to check for missing values, malformed dates, and bad strings in the asset_group field?”
Normalising asset group labels
“My render job export has inconsistent capitalisation and spacing in the asset_group column across 150 rows — can you run string normalisation on it so the labels are clean and consistent?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-02-203?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled data quality operations — audit, normalisation, and outlier detection — on render job records in a single low-cost call. It is ideal for pre-analysis data cleaning of render pipeline exports with up to 200 rows. Prefer it over single-purpose quality tools when you need all three report types simultaneously and the data is already available locally without needing live lookups or predictions.
Side effects
- Input array exceeds 200 rows — request will be rejected or truncated
- Missing required fields (row_id, date, render_seconds, sample_count) cause row-level errors
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Numeric fields outside allowed range trigger schema validation errors
- Empty rows array returns an error response
- field selector not one of 'render_seconds' or 'sample_count' causes enum validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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