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Simulation Caches — Record Quality Audit + String Normalisation + Explicit Missing-Value Fill
x402
Simulation Caches — Record Quality Audit + String Normalisation + Explicit 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 2026-10-22).
Audits record quality, normalises strings, and fills missing values for simulation cache datasets (cache_gb, simulation_seconds) — returning 3 independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Simulation caches — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill 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
Three independent analytical reports: (1) a record quality audit flagging issues such as nulls, out-of-range values, and structural problems; (2) a string normalisation report showing standardised versions of text fields like asset_group; and (3) an explicit missing-value fill report showing imputed values for the chosen numeric field (cache_gb or simulation_seconds) using the selected method (zero, mean, or median). All results are computed locally with no external lookups.
Example use cases
Pre-analysis simulation data cleanup
“I have 120 rows of simulation cache data with asset groups and dates — can you run a full quality audit, normalise any string fields, and fill in any missing cache_gb values using the mean before I start my analysis?”
Detecting nulls in simulation seconds
“Some of my simulation_seconds entries are missing across 80 rows of cache data. Can you audit the record quality, clean up string fields, and fill those gaps using zero imputation so I have a complete dataset?”
Routine data hygiene for cache reporting
“Before I send this batch of 200 simulation cache records to my reporting pipeline, can you do a quality audit, string normalisation, and fill any missing values in the cache_gb column using the median method?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-creative-08-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three bundled data-cleaning reports — quality audit, string normalisation, and missing-value imputation — specifically for simulation cache records (cache_gb, simulation_seconds) in a single low-cost call. It is ideal for lightweight, offline data hygiene tasks on datasets up to 200 rows where no live lookup, prediction, or external data enrichment is required.
Side effects
- Exceeding 200 rows returns a validation error
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Unsupported fill method enum value causes request failure
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
- Missing required fields (row_id, asset_group, date) cause schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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