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Simulation Caches — Record Quality Audit + String Normalisation + Date by Category Pivot
x402
Simulation Caches — 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 2026-10-21).
Runs three local analytical reports on simulation cache data: a record quality audit, string normalisation, and a date-by-category pivot — all on user-supplied rows, no external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Simulation caches — Record quality audit + String normalisation + Date by category pivot: Record quality audit + String normalisation + Date by category pivot for supplied simulation caches records (cache_gb, simulation_seconds). 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 completeness, validity, and anomalies across the supplied rows; (2) a string normalisation report standardising text fields like asset_group; (3) a date-by-category pivot table summarising either cache_gb or simulation_seconds grouped by date and asset_group. All processing is local — no external data is fetched.
Example use cases
Pipeline data quality check
“I've got a batch of simulation cache records from our rendering pipeline — row IDs, asset groups, dates, cache sizes in GB, and simulation seconds. Can you audit the record quality, normalise the string fields, and give me a pivot of cache_gb by date and asset group so I can spot any issues before we archive them?”
Asset group usage breakdown
“Here are 150 rows of simulation cache logs with asset_group, date, cache_gb, and simulation_seconds. Can you pivot the simulation_seconds field by date and category, normalise the asset group strings, and flag any quality problems in the records?”
Pre-report data cleaning
“Before I hand this simulation cache dataset to my team, can you clean it up? Run a record quality audit, normalise the string columns, and give me a date-by-category pivot on cache_gb so the report looks consistent — I have about 80 rows with asset_group, date, cache_gb, and simulation_seconds fields.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-08-206?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical reports — quality audit, string normalisation, and date pivot — on simulation cache data (cache_gb, simulation_seconds) in a single cost-efficient call. Ideal for pre-archival data QA or pipeline reporting on datasets up to 200 rows. Not suitable if you need live external data, predictions, or more than 200 rows processed at once.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, asset_group, date, cache_gb, simulation_seconds) cause row rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- Numeric fields outside allowed range cause schema rejection
- Empty rows array returns an error
- Payment of 0.01 USDC not supplied results in HTTP 402
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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