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Render Jobs Daily Sum Series & Missing-Value Fill
x402
Render Jobs Daily Sum Series & 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 2026-10-17).
Computes a daily sum time series and fills missing values for render job records (render_seconds or sample_count) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Render jobs — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill for supplied render jobs records (render_seconds, sample_count). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two reports: (1) a daily sum time series showing the total of the chosen metric (render_seconds or sample_count) per date, and (2) the same dataset with explicit missing-value fill applied using the chosen method (zero, mean, or median). Up to 200 input rows supported.
Example use cases
Daily render load trend analysis
“I have 150 render job records with dates, asset groups, and render_seconds. Can you sum them up by day and fill any missing dates with the median render time so I can see the daily workload trend?”
Sample count gap filling for pipeline audit
“Our render pipeline has gaps in sample_count data for some days. Can you take our 80 rows of render job data and produce a daily sum series for sample_count, using zero to fill in any missing days?”
Asset group rendering aggregation
“I need to aggregate render_seconds across all our asset groups by day — here are 200 rows of render job logs. Can you run a daily sum series and fill missing values using the mean?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-02-145?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw render job records (with render_seconds or sample_count) and need both a daily aggregated time series and missing-value imputation in a single low-cost call. Ideal for creative/animation pipelines needing lightweight offline analytics without external data lookups or real-time processing.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, date, asset_group) cause row-level errors
- Invalid date format (must be YYYY-MM-DD) causes parse failure
- No rows provided (minItems:1 violated) causes empty-input error
- Null numeric values without a fill method fallback may produce incomplete series
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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