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Render Jobs — Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Render Jobs — Record Quality Audit + String Normalisation + Date by Category Pivot 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).
Audits data quality, normalises strings, and produces a date-by-category pivot for up to 200 render job records (render_seconds, sample_count) in three bundled analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Render jobs — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot 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
Three bundled analytical reports: (1) a record quality audit flagging missing values, out-of-range numbers, or malformed dates; (2) a string normalisation report showing cleaned asset_group and row_id values; and (3) a date-by-category pivot table summarising the chosen numeric field (render_seconds or sample_count) broken down by date and asset group — all derived from the supplied rows, with no external lookups.
Example use cases
Render pipeline data quality check
“I have a batch of render job logs — about 80 rows with asset groups, dates, render_seconds, and sample_count — can you audit them for quality issues, normalise any messy string fields, and pivot render_seconds by date and asset group so I can see the breakdown?”
Animation studio asset group pivot
“We've got 150 render job records from this week's animation run. Can you run a record quality audit on them, clean up the asset_group strings, and give me a pivot table of sample_count grouped by date and category?”
QA report before render data ingestion
“Before I load these 200 render job rows into our data warehouse, can you check them for quality problems, normalise the string fields, and produce a date-by-category pivot on render_seconds so I can spot any outliers?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-02-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have render job records (with render_seconds and/or sample_count) and need three bundled analytical operations — quality audit, string normalisation, and date-category pivot — in a single low-cost call (0.01 USDC). It is ideal for offline/batch data preparation, pre-ingestion QA, or pipeline reporting when no live lookups or external enrichment are needed and the dataset is ≤200 rows.
Side effects
- Rows array empty or missing — returns validation error
- More than 200 rows submitted — rejected with maxItems error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Numeric fields outside allowed range (±1,000,000,000,000) — rejected
- Missing required fields (row_id, asset_group, date) — schema error
- Invalid field enum value (not render_seconds or sample_count) — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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