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Simulation Caches — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Simulation Caches — Record Quality Audit + String Normalisation + Keyed Dataset Comparison 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 records: a record quality audit, string normalisation, and a keyed comparison between two datasets, for up to 200 rows of cache_gb and simulation_seconds data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Simulation caches — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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 reports: (1) a record quality audit flagging issues like nulls, out-of-range values, and malformed fields in cache_gb and simulation_seconds; (2) a string normalisation report standardising text fields like asset_group; and (3) a keyed dataset comparison report showing differences between the primary and secondary input datasets matched by row_id.
Example use cases
Weekly simulation cache data quality check
“Run a full record quality audit on my simulation cache data for this week — I have 150 rows with fields row_id, asset_group, date, cache_gb, and simulation_seconds. Flag any bad or missing values.”
Comparing two simulation cache snapshots
“I have two versions of my simulation cache dataset — the current one and last month's — each up to 200 rows with row_id, asset_group, date, cache_gb, and simulation_seconds. Can you do a keyed comparison to show me what changed?”
Normalising asset group strings before pipeline ingestion
“Before I load these simulation cache records into our pipeline, can you normalise the asset_group strings across all 80 rows so the formatting is consistent? Each row has row_id, asset_group, date, cache_gb, and simulation_seconds.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-08-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three simultaneous analytical reports — quality audit, string normalisation, and dataset comparison — on simulation cache data (cache_gb, simulation_seconds) in a single low-cost call. It is best suited for offline/batch data validation workflows where no live data lookup, prediction, or external action is required, and where your dataset is 200 rows or fewer.
Side effects
- Input rows array is empty or missing — validation error returned
- More than 200 rows submitted — request rejected with item limit error
- Required fields (row_id, asset_group, date, cache_gb, simulation_seconds) missing from one or more rows — schema validation failure
- Date field does not match YYYY-MM-DD pattern — pattern validation error
- Payment not processed via x402 protocol — 402 Payment Required response
- Secondary 'other' dataset missing when keyed comparison is needed — incomplete report or error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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